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DTSTART;TZID=America/Detroit:20201120T150000
DTEND;TZID=America/Detroit:20201120T160000
DTSTAMP:20260607T175150
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000404-1605884400-1605888000@micde.umich.edu
SUMMARY:MICDE / AIM Seminar: Baole Wen\, Assistant Professor\, Mathematics\, University of Michigan
DESCRIPTION:About Baole Wen: Dr. Wen obtained a B.S. degree in Engineering Mechanics and a M.S. degree in Fluid Mechanics\, respectively\, from the Beijing University of Aeronautics and Astronautics.  He was awarded a CEPS Graduate Fellowship \& a Dissertation Year Fellowship and earned a Ph.D. in Applied Mathematics from University of New Hampshire in 2015.  His Ph.D. research was focused on understanding the underlying flow and transport mechanisms governing the spatiotemporally-chaotic system of porous medium convection at large Rayleigh numbers.  Upon graduation\, he was awarded a Peter O’Donnell\, Jr. Postdoctoral Fellowship through the Oden Institute for Computational Engineering and Sciences in the University of Texas at Austin.  His primary research interests are fluid dynamics\, mathematical modeling\, scientific computing and dynamical systems theory.  Recently\, he is working with Dr. Charles Doering as a Postdoctoral Assistant Professor at University of Michigan on extreme behavior in fundamental models of fluid mechanics. \nSTEADY COHERENT STATES IN RAYLEIGH–B\'{E}NARD CONVECTION: Buoyancy-driven flows are central to engineering heat transport\, atmosphere and ocean dynamics\, climate science\, geodynamics\, and stellar physics.   Rayleigh–B\’enard convection—the buoyancy driven flow in a fluid layer heated from below and cooled from above—is recognized as the simplest scenario in which to study such phenomena\, and beyond its importance for applications this problem has served for a century as one of the primary paradigms of nonlinear physics\, complex dynamics\, pattern formation and turbulence.   A central question about Rayleigh–B\’enard convection is how the Nusselt number $Nu$ depends on the Rayleigh number $Ra$ and the Prandtl number $Pr$—i.e.\, how heat flux depends on imposed temperature gradient and the ratio of the fluid’s kinematic viscosity to its thermal diffusivity—as $Ra\rightarrow\infty$.  Experiments and simulations have yet to rule out either `classical’ $Nu \sim Ra^{1/3}$ or `ultimate’ $Nu \sim Ra^{1/2}$ asymptotic scaling.  Here we provide clear quantitative evidence suggesting that the ultimate regime might not exist.  Our tactic is to study relatively simple time-independent states called rolls and compare heat transport by these rolls with that of turbulent convection.  These steady rolls are not typically seen in large-$Ra$ simulations or experiments because they are dynamically unstable.  Nonetheless\, they are part of the global attractor for the infinite-dimensional dynamical system defined by Rayleigh’s model\, and recent results suggest that steady rolls may be one of the key coherent states comprising the `backbone’ of turbulent convection.  By developing novel numerical methods\, we compute steady rolls between no-slip boundaries for $Ra\le 10^{14}$ with $Pr=1$ and various horizontal periods.  We find that rolls of the periods that maximize $Nu$ at each $Ra$ have classical $Nu\sim Ra^{1/3}$ scaling asymptotically\, and they transport more heat than turbulent experiments or simulations at similar parameters.  If turbulent heat transport continues to be dominated by steady transport asymptotically\, it cannot achieve ultimate scaling. \n\nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis event will be a joint seminar with the University of Michigan Applied Interdisciplinary Mathematics. \nQuestions? Email MICDE-events@umich.edu \nJoin the webinar via the Zoom details below:\nhttps://umich.zoom.us/j/96450383843 \nMeeting ID: 964 5038 3843\nPasscode: 010182
URL:https://micde.umich.edu/event/micde-aim-seminar-baole-wen-assistant-professor-mathematics-university-of-michigan/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Baole-Wen.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201203T150000
DTEND;TZID=America/Detroit:20201203T160000
DTSTAMP:20260607T175150
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000401-1607007600-1607011200@micde.umich.edu
SUMMARY:MICDE / IOE Seminar: Salar Fattahi\, Assistant Professor\, Industrial & Operations Engineering\, University of Michigan
DESCRIPTION:About Salar Fattahi: Dr. Salar Fattahi is an Assistant Professor in the Department of Industrial and Operations Engineering at the University of Michigan. He received his M.S. and Ph.D. degrees in Industrial Engineering and Operations Research from UC Berkeley. He received a M.S. degree from Columbia University\, and a B.S. degree from Sharif University of Technology\, Iran\, both in Electrical Engineering. Salar’s research lies at the intersection of optimization\, data analytics\, and control theory. He was the recipient of several awards\, including the 2020 INFORMS ENRE Best Student Paper Award\, 2018 INFORMS Data Mining Best Paper Award and 2020 Power & Energy Society General Meeting Best-of-the-Best Paper Award. He was also a finalist for the 2018 American Control Conference Best Paper Award. \nWebinar: LARGE-SCALE INFERENCE OF TIME-VARYING MARKOV RANDOM FIELDS: BRIDGING THE GAP BETWEEN STATISTICAL AND COMPUTATIONAL EFFICIENCIES \nContemporary systems are comprised of a massive number of interconnected components that interact according to a hierarchy of complex\, dynamic\, and unknown topologies. For example\, with billions of neurons and hundreds of thousands of voxels\, the human brain is considered as one of the most complex physiological networks\, whose structure remains as a long-standing mystery. As another example\, the emergence of self-driving cars has only accentuated the need for the development of real-time and reliable methods for detecting moving objects\, whose temporal locations are captured through a dynamically-evolving 3D network. Nonetheless\, the vast amounts of parameters to be estimated\, caused both by the large number of components and the time-varying nature of the systems\, are currently the major bottlenecks in our ability to successfully solve such inference problems. \nThe temporal behavior of today’s interconnected systems can be captured via time-varying Markov random fields (MRF). A popular approach to achieve this goal is based on the so-called maximum-likelihood estimation (MLE): to find a probabilistic graphical model\, based on which the observed data is most probable to occur. The MLE-based methods suffer from several fundamental drawbacks which render them impractical in realistic settings. First\, they often suffer from notoriously high computational cost in the massive problems\, where the number of variables to be inferred is in the order of millions\, or more. Second\, they fail to efficiently incorporate prior structural information into their estimation procedure. With the goal of bridging this knowledge gap\, the aim of this work is to revisit the standard MLE as the “Holy Grail” of the inference methods for graphical models\, and precisely pinpoint and remedy the scenarios where it fails. A recurring theme in our proposed approach is a class of efficiently-solvable mixed-integer optimization problems that is used in lieu of the regularized MLE for the inference of time-varying MRFs. Our proposed optimization problems enjoy strong statistical and computational guarantees\, while being amenable to a wide class of graphical models with different side information\, such as sparsity\, smoothness\, etc. \n\nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis event will be a joint seminar with the University of Michigan College of Engineering’s Industrial Operations & Engineering department. \nQuestions? Email MICDE-events@umich.edu \nConnect via this Zoom link: https://umich.zoom.us/j/96516676892#success
URL:https://micde.umich.edu/event/micde-ioe-seminar-salar-fattahi-assistant-professor-industrial-operations-engineering-university-of-michigan/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Salar-Fattahi.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210119T160000
DTEND;TZID=America/Detroit:20210119T170000
DTSTAMP:20260607T175150
CREATED:20230905T171257Z
LAST-MODIFIED:20230905T171257Z
UID:10000427-1611072000-1611075600@micde.umich.edu
SUMMARY:MICDE Seminar: Yang Liu\, Research Scientist\, Scalable Solvers Group of the Computational Research Division at the Lawrence Berkeley National Laboratory
DESCRIPTION:About Dr. Liu: Yang Liu is a research scientist in the Scalable Solvers Group of the Computational Research Division at the Lawrence Berkeley National Laboratory\, in Berkeley\, California. Dr. Liu received the Ph.D. degree in electrical engineering from the University of Michigan in 2015. From 2015 to 2017\, he worked as a postdoctoral fellow at the Radiation Laboratory\, University of Michigan. From 2017 to 2019\, he worked as a postdoctoral fellow at the Lawrence Berkeley National Laboratory. His main research interest is in numerical linear and multi-linear algebras (including sparse solvers\, randomized low-rank\, butterfly and tensor algebras)\, computational electromagnetics (including fast iterative time-domain integral equation solvers\, fast direct integral and differential equation solvers\, and multi-physics\nmodeling)\, scalable machine learning algorithms\, and high-performance scientific computing. Dr. Liu authored and co-authored the Sergei A. Schelkunoff Transactions Prize Paper\, APS 2018\, second place student paper\, ACES 2012\, and the first place student paper\, FEM 2014. \nFAST\, DIRECT INTEGRAL DIFFERENTIAL EQUATION SOLVERS FOR ELECTROMAGNETIC ACOUSTIC\, AND ELASTIC APPLICATIONS AT ALL FREQUENCY RANGES: Large-scale and full-wave modeling for acoustic and elastic inversion applications\, analysis and synthesis of electromagnetic systems for traditional and emerging RF\, microwave\, terahertz applications rely on efficient numerical tools. Integral equation (i.e.\, method of moment) and differential equation (e.g.\, finite-difference\, finite-element\, and finite-volume) formulations lead to dense and sparse linear systems\, respectively. These linear systems can be solved by either iterative or direct solvers. Iterative solvers\, despite their success in constructing well-conditioned formulations and fast multipole-type algorithms\, remain inefficient for systems that are inherently ill-conditioned and/or require multiple right-hand sides. This is particularly true for design automation\, inverse scattering\, and other coupled systems where iterative solvers often require forbiddingly high iteration time. Direct solvers\, in stark contrast\, can attain reliable solutions in a predictable time. However\, exact direct solvers typically require O(N 3 ) and O(N 2 ) computational costs for dense and sparse systems of size N\, respectively. Fast direct solvers\, on the other hand\, rely on the fact that off-diagonal blocks of the well-ordered linear systems can be compressed by numerical linear algebra tools including low-rank and butterfly decompositions. When further embedded in hierarchical matrix frameworks\, such as H-matrix\, hierarchically off-diagonal low-rank (HODLR)\, and hierarchically semi-separable (HSS) formats\, these direct solvers and preconditioners can achieve quasi-linear complexities for construction\, factorization and solution for the discretized systems across all frequency ranges. We will review the development of these solvers in the past two decades\, with an emphasis on their butterfly-based variants and distributed-memory parallelization for high-frequency problems. An open source package integrating most techniques reviewed\, called ButterflyPACK\, will also be introduced. \n\nWatch the full webinar. \nNote: You can register after the webinar has started.
URL:https://micde.umich.edu/event/micde-aim-seminar-yang-liu-research-scientist-scalable-solvers-group-of-the-computational-research-division-at-the-lawrence-berkeley-national-laboratory/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/11/Yang-Liu.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210126T150000
DTEND;TZID=America/Detroit:20210126T160000
DTSTAMP:20260607T175150
CREATED:20230905T171256Z
LAST-MODIFIED:20230905T171256Z
UID:10000406-1611673200-1611676800@micde.umich.edu
SUMMARY:MICDE Seminar: Tianle Yuan\, Associate Research Scientist\, University of Maryland\, Baltimore County\, JCET\, NASA Goddard Space Flight Center
DESCRIPTION:About Dr. Tianle Yuan: Dr. Yuan received his B.S. in Geophysics and Computer Science from Peking University\, his Ph.D. from the University of Maryland\, College Park in 2008. After graduation\, he became affiliated with the Joint Center for Earth Systems Technologies (JCET) at the University of Maryland Baltimore County (UMBC) and NASA Goddard Space Flight Center (GSFC) as an Associate Research Scientist. His research interests include cloud and aerosol climate feedback\, aerosol-cloud interactions\, remote sensing\, cloud physics\, and application of ML/Deep Learning in Earth science. In deep learning applications\, Dr. Yuan published a few papers in modeling sub-grid clouds\, global scale clouds\, hurricane prediction\, finding ship-tracks\, and supervised and unsupervised cloud morphology classifications. \nARTIFICIAL INTELLIGENCE-BASED CLOUD DISTRIBUTOR (AI-CD): MODELING CLOUDS AT DIFFERENT SCALES\nHere we introduce the artificial intelligence-based cloud distributor (AI-CD) approach to generate cloud fields across different scales and cloud types. We show that generative adversarial nets (GANs) can not only generate realistic cloud fields with corresponding meteorological variables\, but also capture known physical relationship between cloud fields and meteorological variables such as sea surface temperature\, atmospheric stability\, and relative humidity etc. We demonstrate that this approach works across a large range of spatial scales: from individual grid points (sub-grid process modeling)\, multiple grids\, to global scale. In addition\, the AI-CD approach is stochastic in nature. We suggest the AI-CD approach can be used as a data-drive framework for stochastic cloud parameterization. \n\nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nRegister to immediately receive Zoom details. Note: you may register after the event has started. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-tianle-yuan-research-associate-nasa-goddard-space-flight-center/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Tianle-Yuan.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210204T110000
DTEND;TZID=America/Detroit:20210204T120000
DTSTAMP:20260607T175150
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000407-1612436400-1612440000@micde.umich.edu
SUMMARY:MICDE / MIDAS Seminar: Ivo Dinov\, Professor\, Nursing\, Computational Medicine & Bioinformatics
DESCRIPTION:Bio: Dr. Ivo D. Dinov directs the Statistics Online Computational Resource (SOCR)\, co-directs the multi-institutional Probability Distributome Project\, and is an associate director for education of the Michigan Institute for Data Science (MIDAS). \nDr. Dinov is an expert in mathematical modeling\, statistical analysis\, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g.\, Autism\, Schizophrenia)\, maturation (e.g.\, depression\, pain) and aging (e.g.\, Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing\, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning. \nDATA SCIENCE\, TIME COMPLEXITY\, AND SPACEKIME ANALYTICS \nMany observable processes demand managing\, harmonizing\, modeling\, analyzing\, interpreting\, and visualizing of large and complex information. There is a substantial need to develop\, validate\, productize\, and support novel mathematical techniques\, advanced statistical computing algorithms\, transdisciplinary tools\, and effective artificial intelligence applications. Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of time\, events\, particles\, and wavefunctions to complex-time (kime)\, complex-events (kevents)\, data\, and inference-functions. We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference. \nThe mathematical foundation of spacekime calculus reveal various statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes from the classical 4D Minkowski spacetime to a 5D spacekime manifold\, where a number of interesting mathematical problems arise. Direct data science applications of spacekime analytics will be demonstrated using simulated data and clinical observations (e.g.\, structural and functional MRI). \n\nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nWatch the recorded webinar. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-ivo-dinov-professor-nursing-and-computational-medicine-bioinformatics-university-of-michigan/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Ivo-Dinov.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210209T160000
DTEND;TZID=America/Detroit:20210209T170000
DTSTAMP:20260607T175150
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000408-1612886400-1612890000@micde.umich.edu
SUMMARY:MICDE / Mechanical Engineering Seminar: Ceila Reina\, Assistant Professor\, Mechanical Engineering and Applied Mechanics\, University of Pennsylvania
DESCRIPTION:Bio:  Celia Reina is the William K. Gemmill Term Assistant Professor in Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. She joined in 2014 after holding the Lawrence Postdoctoral Fellowship at Lawrence Livermore National Laboratory and the HCM postdoctoral Fellowship at the Hausdorff Center of Mathematics in Bonn\, Germany. Dr. Reina received her PhD from the California Institute of Technology in Aerospace Engineering in 2011\, under the supervision of Prof. Michael Ortiz\, following a B.S. in Mechanical Engineering from the University of Seville in Spain\, and a Master in Structural Dynamics from Ecole Centrale Paris in France. She is the 2017 recipient of the Eshelby Mechanics Award for Young Faculty\, she is a member of the TTA on Nanotechnology and Lower Scale Phenomena at the USACM\, and she currently serves as the recording secretary for the Applied Mechanics Division of the ASME. \nCONTINUUM MECHANICS OF NON-EQUILIBRIUM PHENOMENA: A JOURNEY THROUGH SPACE AND TIME SCALES:  The fascinating diversity of material behavior at the macroscopic scale can only emerge from the underlying atomistic or particle behavior. Yet\, the direct connection between these two scales remains an extremely challenging quest\, particularly in the context of non-equilibrium phenomena. In this talk\, we will discuss several advances in this direction\, in the context of plasticity\, thermoelasticity\, diffusion and viscous dissipation. In all these cases\, the importance of fluctuations in the effective response will become apparent. More precisely\, these will provide crucial information for the material description and evolution at the continuum scale\, where the behavior is modeled as deterministic and free of fluctuations. \n\nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis event will be a joint seminar with the University of Michigan College of Engineering’s Mechanical Engineering department. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-mechanical-engineering-seminar-ceila-reina-assistant-professor-mechanical-engineering-and-applied-mechanics-university-of-pennsylvania/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Celia-Reina.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210216T150000
DTEND;TZID=America/Detroit:20210216T160000
DTSTAMP:20260607T175150
CREATED:20230905T171258Z
LAST-MODIFIED:20230905T171258Z
UID:10000428-1613487600-1613491200@micde.umich.edu
SUMMARY:MICDE Seminar: Emma Lejeune\, Assistant Professor\, Mechanical Engineering\, Boston University
DESCRIPTION:Bio: Emma Lejeune is an Assistant Professor in the Mechanical Engineering Department at Boston University. She received her PhD from Stanford University in September 2018\, and was a Peter O’Donnell\, Jr. postdoctoral research fellow at the Oden Institute at the University of Texas at Austin until 2020 when she joined the faculty at BU. At BU\, Emma has received the David R. Dalton Career Development Professorship\, a Computational Science and Engineering Junior Faculty Fellowship\, and the Haythornthwaite Research Initiation Grant from the ASME Applied Mechanics Division. Current areas of research involve integrating data-driven and physics based computational models\, and characterizing and predicting the mechanical behavior of heterogeneous materials and biological systems. \nMODELING HETEROGENEOUS MATERIALS: BENCHMARK DATASETS\, METAMODELS\, AND EXPERIMENTAL CHARACTERIZATION: \nBiological systems are spatially heterogeneous across scales. To effectively model biological materials we need new tools to quantify and capture this heterogeneity. In this talk\, we will first discuss our recent work on simulating spatially heterogeneous materials. Specifically\, we will discuss our recent work in developing and exploring benchmark datasets of spatially heterogeneous materials simulated with the finite element method. These datasets are useful primarily for constructing metamodels\, or computationally cheap models of models\, that map defined model inputs to defined model outputs. By nature\, a given metamodel will be tailored to a specific dataset. However\, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present\, the most pragmatic metamodel selection for predicting the mechanical behavior of spatially heterogeneous materials — specifically simulations of heterogenous materials — has not been thoroughly explored. Drawing inspiration from the benchmark datasets available to the computer vision research community\, we introduce a benchmark data set (Mechanical MNIST https://open.bu.edu/handle/2144/39371) for constructing metamodels of heterogeneous material undergoing large deformation. We then show a few examples of problems that we have explored thus far with this dataset. Looking forward\, we anticipate that disseminating benchmark datasets will enable the broader community of researchers to develop improved metamodeling techniques for capturing the behavior of spatially heterogeneous materials that will surpass the baseline performance that we show here. Finally\, to conclude the talk\, we will change gears and briefly discuss some of our recent work on creating new tools for characterizing cell behavior using concepts from kinematics and spatial statistics. Looking forward\, we are interested in the natural synergy between advances in methods for both simulating and characterizing heterogeneous materials. \n\nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nDr. Lejeune will be hosted by Professor Krishna Garikipati\, MICDE Director. \nWatch the full webinar. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-emma-lejeune-assistant-professor-mechanical-engineering-boston-university/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/11/Emma-Lejeune.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210301T130000
DTEND;TZID=America/Detroit:20210301T140000
DTSTAMP:20260607T175150
CREATED:20230905T171259Z
LAST-MODIFIED:20230905T171259Z
UID:10000450-1614603600-1614607200@micde.umich.edu
SUMMARY:MICDE Seminar: Santo Fortunato\, Director of the Indiana University Network Science Institute (IUNI)\, Professor\, School of Informatics\, Computing\, and Engineering (SICE)\, Indiana University at Bloomington
DESCRIPTION:About Dr. Fortunato: Santo Fortunato is the Director of the Indiana University Network Science Institute (IUNI) and a faculty at Luddy School of Informatics\, Computing and Engineering. Previously he was professor of complex systems at the Department of Computer Science of Aalto University\, Finland. Prof. Fortunato got his PhD in Theoretical Particle Physics at the University of Bielefeld In Germany. He then moved to the field of complex systems\, via a postdoctoral appointment at Luddy School of Informatics\, Computing and Engineering of Indiana University. His current focus areas are network science\, especially community detection in graphs\, computational social science\, science of science\, climate change. His research has been published in leading journals\, including Nature\, Science\, PNAS\, Physical Review Letters\, Reviews of Modern Physics\, Physics Reports and has collected over 33\,000 citations (Google Scholar). His review article Community detection in graphs (Physics Reports 486\, 75-174\, 2010) is one of the best known and most cited papers in network science. He received the Young Scientist Award for Socio- and Econophysics 2011\, a prize given by the German Physical Society\, for his outstanding contributions to the physics of social systems. He is the Founding Chair of the International Conference on Computational Social Science (IC2S2) and Chair of Networks 2021\, the first merger of the NetSci and the Sunbelt conferences\, possibly the largest ever event in network science. \nCOMMUNITY DETECTION IN NETWORKS: Complex systems typically display a modular structure\, as modules are easier to assemble than the individual units of the system\, and more resilient to failures. In the network representation of complex systems\, modules\, or communities\, appear as subgraphs whose nodes have an appreciably larger probability to get connected to each other than to other nodes of the network. In this talk I will discuss three main issues in this area. I will address the limits of the most popular class of clustering algorithms\, those based on the optimization of a global quality function\, like modularity maximization. Testing algorithms is probably the single most important issue of network community detection\, as it implicitly involves the concept of community\, which is ill-defined. I will discuss the importance of using realistic benchmark graphs with built-in community structure. Finally\, I will introduce an increasingly popular post-processing technique that allows to “average” the results of stochastic clustering algorithms\, improving their quality: consensus clustering. \n\nWatch the full webinar recording. \nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-santo-fortunato-director-of-the-indiana-university-network-science-institute-iuni-professor-school-of-informatics-computing-and-engineering-sice-indiana-university-at-blooming/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/01/Santo-Fortunato.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210311T140000
DTEND;TZID=America/Detroit:20210311T150000
DTSTAMP:20260607T175150
CREATED:20230905T171300Z
LAST-MODIFIED:20230905T171300Z
UID:10000457-1615471200-1615474800@micde.umich.edu
SUMMARY:MICDE Seminar: Warren B. Mori\, Professor\, Physics and Astronomy\, Electrical and Computer Engineering\, University of California\, Los Angeles
DESCRIPTION:WATCH THE RECORDING HERE. \nBio: Warren B. Mori is a Distinguished Professor in the departments of Physics and Astronomy and of Electrical and Computer Engineering a UCLA. He received his BS from UC Berkeley in 1981\, and his M.S. and Ph.D. from UCLA in 1984 and 1987\, respectively. He has been at UCLA from 1981 until today. He served as the Director of the UCLA Institute for Digital Research and Education from 2006 until 2021. His current research interests are in advanced computing\, particle-in-cell simulations of plasmas\, basic plasma physics\, high intensity laser and beam plasma interactions\, plasma based accelerators and light sources\, nonlinear optics of plasmas\, inertial fusion science\, and high energy density science. He is the coauthor of more than 400 publications on a variety of topics in plasma and computational physics. He is a fellow of both APS (1997) and IEEE (2009) and is a current member of both societies. In 1987 he received the International Center for Theoretical Physics Medal for Excellence in Nonlinear Plasma Physics by a Young Researcher was a recipient of the Advanced Accelerator Concepts Prize in 2016 for\, “ his leadership and pioneering contributions in theory and particle-in-cell code simulations of plasma based particle acceleration.” In 2020 he received the APS James Clerk Maxwell prize for\, “leadership in and pioneering contributions to the theory and kinetic simulations of nonlinear processes in plasma-based acceleration and relativistically intense laser and beam plasma interactions. \nPLASMA BASED ACCELERATION AND THE ROLE OF HIGH FIDELITY SIMULATIONS IN ITS DEVELOPMENT\nParticle accelerators are critical components of high energy physics colliders and x-ray free electron lasers (XFELs)\, which are complex and expensive tools for scientific discovery. To reduce the size and cost of these tools there is active research aimed at finding new technologies for compact accelerators. One such possibility is the use of plasma waves which phase velocities near the speed of light that can be excited as wakefields behind intense lasers and particle beams as they traverse tenuous plasmas. These ideas are the basis for the field of plasma based acceleration (PBA). In this talk I will describe how PBA works\, and how high fidelity computer simulations have and are playing a critical role in its development. I will also describe the simulation methods and their associated algorithms. Last\, I will offer some perspectives for the future of plasma based acceleration and the simulation methods that will critical role in this future. Work supported by DOE and NSF.\n \n\nThis seminar is co-presented by the Michigan Institute for Computational Discovery & Engineering and the Michigan Institute for Plasma Science and Engineering. Dr. Mori will be hosted by Professor Alec Thomas\, Professor of Nuclear Engineering and Radiological Sciences\, Electrical Engineering and Computer Science\, and Physics. \nRegister for this event via Zoom to receive an email with the link and passcode to connect. Note: You may register after the event has started. \nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-warren-mori-professor-physics-and-astronomy-electrical-and-computer-engineering-university-of-california-los-angeles/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/02/Warren-Mori.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210318T110000
DTEND;TZID=America/Detroit:20210318T120000
DTSTAMP:20260607T175150
CREATED:20230905T171300Z
LAST-MODIFIED:20230905T171300Z
UID:10000462-1616065200-1616068800@micde.umich.edu
SUMMARY:MICDE Seminar: Udo von Toussaint\, PD\, Group Leader at the Max-Planck-Institute for Plasmaphysics in Garching\, Divison Numerical Methods for Plasmaphysics
DESCRIPTION:WATCH THE RECORDING HERE. \nBio: Dr. Udo v. Toussaint earned his PhD in Physics at the University of Bayreuth in 2000. He then worked as a Postdoctoral Researcher at NASA Ames (RIACS)\, in Mountain View\, CA from 2000-2002.  Since 2003\, Dr. von Toussaint has been a Scientist at the Max-Planck Institute for Plasmaphysics in Garching. Dr. von Toussaint is also editor of the ‘Entropy’ journal. \nBesides plasma-wall interaction\, his research interests are focussed on the design of optimal analysis and measurement strategies (Bayesian experimental design) for computer- and physics experiments. This encompasses modern concepts of uncertainty quantification (UQ) of complex computer codes (e.g. Plasma-wall simulations) as well as active-learning systems\, which dynamically decide which action (e.g. measurement of a specific spectral line) might yield the most informative data based on the results from previous actions. This is addressed with Machine Learning techniques\, e.g. Hidden Markov Models (HMM)\, neutral networks or bayesian acyclic graphs and complemented by numerical methods like Markov Chain Monte Carlo (MCMC)\, sequential optimization or polynomial chaos expansion. \nA BAYESIAN APPROACH TO ARTIFICIAL NEURAL NETWORKS:  Artificial Neural networks (ANN) are famous for their advantageous flexibility for problems when there is insufficient knowledge to set up a proper model. On the other hand\, this flexibility can cause overfitting and can hamper the generalization and stability of ANNs. Many approaches to regularize ANNs have been suggested (e.g. L1- or L2-norm based regularization) but most of them are based on ad hoc arguments. Employing the principle of transformation invariance\, a general prior for feed-forward networks can be derived. This regularization prior not only favours cell and layer pruning but enable also a consistent Bayesian approach: Relying on Occam’s razor we demonstrate (as a proof of concept) how an ANN can be applied even in the >absence< of available training data. The relation to the concept of automatic relevance detection will be discussed. \n\nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nDr. von Toussaint will be hosted by Professor Xun Huan\, Assistant Professor of Mechanical Engineering. \nRegister for this event via Zoom to receive an email with the link and passcode to connect. Note: You may register after the event has started. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-udo-von-toussaint-pd-group-leader-at-the-max-planck-institute-for-plasmaphysics-in-garching-divison-numerical-methods-for-plasmaphysics/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/03/Udo-von-Toussaint.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210319T150000
DTEND;TZID=America/Detroit:20210319T160000
DTSTAMP:20260607T175150
CREATED:20230905T171444Z
LAST-MODIFIED:20230905T171444Z
UID:10000464-1616166000-1616169600@micde.umich.edu
SUMMARY:AIM/MICDE Seminar: Daniel Lecoanet\, Postdoctoral Fellow\, Princeton Center for Theoretical Science\, Astrophysical Sciences\, Princeton University
DESCRIPTION:Bio: Dr. Lecoanet earned a Bachelor of Science degree in Mathematics from the University of Wisconsin at Madison\, a Master’s degree in Applied Mathematics from the University of Cambridge\, and a PhD in Physics from the University of California\, Berkeley. He currently holds a joint postdoc position at the Princeton Center for Theoretical Science and as a Lyman Spitzer\, Jr. fellow at the Department of Astrophysical Sciences. Dr. Lecoanet works primarily on Astrophysical and Geophysical Fluid Dynamics. He is a core developer for Dedalus. \nPROBING THE CORES OF MASSIVE STARS THROUGH THEIR SURFACE: Stars are opaque\, which makes it difficult to study their interiors. Recent space-based telescopes have led to the new field of asteroseismology: by measuring global oscillation modes of a star\, you can infer its interior properties. Massive stars have convection in their cores which can generate waves\, which might be detectable at the surface. In the first part of this talk\, I will describe a heuristic way of estimating wave generation by convection\, and compare it to high-resolution numerical simulations in Cartesian geometry. To make quantitative predictions to compare with observations\, one must run simulations in spherical geometry. In the second part of my talk\, I will present a new spectral algorithm for solving nearly arbitrary\, tensorial PDEs in spherical coordinates. The challenge is to devise bases which respect regularity conditions at r=0\, which depend on the rank of the tensor. The algorithm can be easily applied to the problem of wave generation by convection in stars\, as well as a wide range of other problems in stellar astrophysics\, core geophysics\, and planetary sciences. \n\nThis seminar is co-presented by Applied and Interdisciplinary Mathematics program\, and the Michigan Institute for Computational Discovery & Engineering. Dr. Lecoanet will be hosted by Professor Charlie Doering\, the Nicholas D. Kazarinoff Collegiate Professor of Complex Systems\, Mathematics and Physics\, and Director of the Center for the Study of Complex Systems. \nRegister for this event to receive Zoom login information. \nThe MICDE Winter 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-daniel-lecoanet-postdoctoral-fellow-princeton-center-for-theoretical-science-astrophysical-sciences-princeton-university/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20211104T120000
DTEND;TZID=America/Detroit:20211104T130000
DTSTAMP:20260607T175150
CREATED:20210923T030754Z
LAST-MODIFIED:20230713T171653Z
UID:10000524-1636027200-1636030800@micde.umich.edu
SUMMARY:MICDE / SPH: Laura Matrajt\, Staff Scientist\, Vaccine and Infectious Disease Division\, Fred Hutch
DESCRIPTION:Bio: Dr. Matrajt is a Staff Scientist in the Vaccine and Infectious Disease Division at the Fred Hutch research center in Seattle. She is an applied mathematician passionate about utilizing quantitative tools (mathematical and computer models\, statistics\, optimization theory) to understand complex biological processes. Her research lies at the interface of applied mathematics\, biology and public health policy. Dr. Matrajt uses a wide range of tools from applied mathematics including dynamical systems\, differential equations\, stochastic processes\, operations research and optimization theory to forward our understanding of infectious disease dynamics. \nDr. Matrajt was born and raised in Mexico City\, Mexico. She attended UNAM\, where she studied Mathematics as an undergraduate. Dr. Matrajt moved to Seattle\, WA\, where she completed a PhD in the Applied Mathematics Department at the University of Washington\, where she graduated in 2011. \nOptimizing COVID-19 vaccine allocation\nVaccines have proven to be our best tool to control the current COVID-19 pandemic. However\, due to limited vaccine supply\, vaccine prioritization has been\, and continues to be\, unavoidable. In this talk\, I will discuss two projects that used mathematical modeling combined with a fast optimization algorithm to determine the optimal use of these precious resources. In the first one\, we determined who should be vaccinated first\, and showed that the optimal use of COVID-19 vaccine depends on vaccine efficacy and vaccination coverage. In the second project we considered who should be vaccinated and how many doses they should get\, and found that optimal allocation strategies with one or two doses of vaccine depend on the efficacy after the first dose\, the background viral transmission and the amount of vaccine available. \n\nWATCH THE RECORDING HERE. \n\nThe MICDE Fall 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the department of Epidemiology within the School of Public Health at the University of Michigan. Dr. Matrajt will be hosted by Dr. Rafael Meza\, Professor of Epidemiology and Global Public Health. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-sph-laura-matrajt-ph-d-scientist-vaccine-and-infectious-disease-division-fred-hutchinson-cancer-research-center/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/09/Laura-Matrajt.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20211203T150000
DTEND;TZID=America/Detroit:20211203T160000
DTSTAMP:20260607T175150
CREATED:20210923T031753Z
LAST-MODIFIED:20230713T171452Z
UID:10000525-1638543600-1638547200@micde.umich.edu
SUMMARY:MICDE / AIM: Youngsoo Choi\, Research Scientist\, Center for Applied Scientific Computing\, Lawrence Livermore National Laboratory
DESCRIPTION:Zoom link | Meeting ID: 964 5038 3843 | Psswd: 010182 \n\nBio: Youngsoo is a computational math scientist in CASC under Computing directorate at LLNL. He is currently leading data-driven reduced order model development team for various physical simulations\, with whom he developed the open source codes\, libROM (https://www.librom.net) and LaghosROM (https://github.com/CEED/Laghos/tree/rom/rom). libROM is a library for reduced order models and LaghosROM implements reduced order models for Lagrangian hydrodynamics (https://authors.elsevier.com/c/1e3CuAQEIviQh). He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University with applied mathematics as minor and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc in Sandia National Laboratory and Stanford University prior to joining LLNL in 2017. \nPhysics-constrained data-driven methods for accurately accelerating simulations\nA data-driven model can be built to accurately accelerate computationally expensive physical simulations\, which is essential in multi-query problems\, such as inverse problem\, uncertainty quantification\, design optimization\, and optimal control. In this talk\, two types of data-driven model order reduction techniques will be discussed\, i.e.\, the black-box approach that incorporates only data and the physics-constrained approach that incorporates the first principle as well as data. The advantages and disadvantages of each method will be discussed. Several recent developments of generalizable and robust data-driven physics-constrained reduced order models will be demonstrated for various physical simulations as well. For example\, a hyper-reduced time-windowing reduced order model overcomes the difficulty of advection-dominated shock propagation phenomenon\, achieving a speed-up of O(20~100) with a relative error much less than 1% for Lagrangian hydrodynamics problems\, such as 3D Sedov blast problem\, 3D triple point problem\, 3D Taylor–Green vortex problem\, 2D Gresho vortex problem\, and 2D Rayleigh–Taylor instability problem. The nonlinear manifold reduced order model also overcomes the challenges posed by the problems with Kolmogorov’s width decaying slowly by representing the solution field with a compact neural network decoder\, i.e.\, nonlinear manifold. The space–time reduced order model accelerates a large-scale particle Boltzmann transport simulation by a factor of 2\,700 with a relative error less than 1%. Furthermore\, successful application of these reduced order models for mate-material lattice–structure design optimization problems will be presented. Finally\, the library for reduced order models\, i.e.\, libROM (https://www.librom.net)\, and its webpage and several YouTube tutorial videos will be introduced\, which is useful for education as well as research purpose. \n\n  \nThe MICDE Fall 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Applied and Interdisciplinary Mathematics program at the University of Michigan. Dr. Choi will be hosted by Dr. Jesse Capecelatro\, Assistant Professor of Mechanical Engineering and Aerospace Engineering. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-aim-youngsoo-choi-llnl/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/09/Youngsoo-Choi.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20211208T150000
DTEND;TZID=America/Detroit:20211208T160000
DTSTAMP:20260607T175150
CREATED:20210923T032752Z
LAST-MODIFIED:20230713T171305Z
UID:10000526-1638975600-1638979200@micde.umich.edu
SUMMARY:MICDE Seminar: Sarah Hormozi\, Associate Professor\, Cornell University
DESCRIPTION:WATCH THE RECORDING HERE. \n\nBio: Sarah Hormozi is an associate professor of Chemical and Biomolecular Engineering at Cornell University. Her expertise lies in complex fluid mechanics\, rheology\, and soft matter physics. Her research has been recognized by a number of awards\, including the National Science Foundation CAREER award and the ACS Petroleum Research Fund Doctoral New Investigator Award. She also serves on the advisory boards of Journals of Physical Review Fluids\, Non-Newtonian Fluid Mechanics\, The American Institute of Chemical Engineers\, and Physics of Fluids. \nSlurries of complex fluids\nSuspensions of non-Brownian particles in viscous fluids\, for which thermal fluctuations are negligible\, are relevant in industrial processes (e.g. waste disposal\, concrete\, drilling muds\, metalworking chip transport\, and food processing) and in natural phenomena (e.g. flows of slurries\, debris\, and lava). It is also relevant to mention that some biological and smart materials can be designed from various suspensions\, drawing attention to applications in physiology\, bio\nlocomotion\, shock absorbers\, and beyond. This countless number of suspensions has a wide range of nonlinear rheological behaviors\, such as shear thinning\, shear thickening\, shear banding\, yield stress\, and finite normal stress differences even when inertia is negligible.\nFor applications enumerated above\, even small increases in efficiency when processing slurries of complex fluids could make significant positive economic and environmental impacts. Obviously\, a thorough understanding of the rheology and fluid mechanics of these materials in natural and industrial settings is essential to improving the efficiency of production. However\, this is extremely challenging due to the complex rheology of the suspending fluids\, the interaction of fluid and particle phases\, and multiple-body and short-range interactions of particles. My presentation will introduce an array of experimental and modeling techniques that my research team uses to investigate the rheological properties and fluid dynamical behavior of complex suspensions. The goal is to establish a continuum framework and refine it through a series of microstructure investigations. I will discuss how our recent results can be used to address and resolve some of the industrial issues. Finally\, open questions will be disclosed\, which must be answered to build a firm foundation for a long-term contribution to the area of complex suspensions. \n\nThe MICDE Fall 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is hosted by the Michigan Institute for Computational Discovery and Engineering (MICDE) at the University of Michigan. Dr. Hormozi will be hosted by Dr. Mariana Carrasco-Teja\, MICDE Associate Director and Assistant Research Scientist. Questions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-sarah-hormozi-ph-d-associate-professor-smith-school-of-chemical-and-biomolecular-engineering-cornell-university/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/09/Sarah-Hormozi.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20211208T160000
DTEND;TZID=America/Detroit:20211208T170000
DTSTAMP:20260607T175150
CREATED:20211129T204202Z
LAST-MODIFIED:20230713T171133Z
UID:10000540-1638979200-1638982800@micde.umich.edu
SUMMARY:James Stokes\, Research Fellow\, Flatiron Institute
DESCRIPTION:In person event (no zoom available)!! \n\nBio: Dr. James Stokes is a Research Fellow at the Flatiron Institute with a joint position at the Computational Center for Quantum Physics and the Center for Computational Mathematics.  He completed his Ph.D at the University of Pennsylvania\, focusing on quantum field theory. His recent research intersects machine learning\, quantum information and condensed matter physics. Previously\, James was a postdoctoral researcher in the Computer and Information Science Department at University of Pennsylvania from 2017 to 2018. James also worked as a quant in the finance industry before his postdoc and as a research scientist at a machine learning startup. \n  \nGeometry and numerics of variational quantum algorithms and classical counterparts\nStokes will review a family of variational algorithms which have been proposed as candidates to make use of near- to intermediate-term quantum computers\, placing particular emphasis on geometric and numeric features that are shared by classical variational stochastic approximation algorithms. Stokes will also discuss some applications of this hybrid quantum-classical approach to scientific and engineering problems beyond its traditional domain of application. \n  \n\nThis seminar is co-hosted by the Michigan Center for Applied & Interdisciplinary Mathematics (MCAIM) and the Michigan Institute for Computational Discovery and Engineering (MICDE) at the University of Michigan. Dr. Stokes will be hosted by Prof. Shravan Veerapaneni\, professor of mathematics. Questions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/james-stokes-flatiron-fall2021/
LOCATION:B844 East Hall\, 530 Church St\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/11/James-Stokes.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220225T150000
DTEND;TZID=America/Detroit:20220225T160000
DTSTAMP:20260607T175150
CREATED:20230714T154821Z
LAST-MODIFIED:20230714T154821Z
UID:10000604-1645801200-1645804800@micde.umich.edu
SUMMARY:MICDE / AIM Seminar: Blaise Bourdin\, PhD\, Professor of Mathematics & Statistics\, McMaster University
DESCRIPTION:Zoom Link \nBio: Blaise Bourdin is a professor in the department of mathematics and statistics at McMaster University (Hamilton\, ON\, Canada). Dr. Bourdin’s formal training is at the meeting point of solid mechanics\, scientific computing\, and applied mathematics. He borrows techniques for these areas to study problems in mechanical science with a specific focus on problems involving defect mechanics and optimal design. He has cultivated this multi-disciplinary training by revisiting “classical” problems with more advanced technical tools\, by skewing my theoretical work towards problems of particular relevance to engineering and science\, and by using investigative numerical simulations as a modeling tool in complex multi-scale problems. \nDr. Bourdin’s research focusses on modeling\, analysis\, and numerical implementations of problems arising in reservoir engineering\, defect mechanics\, optimal design\, and image processing. He is the recipient of multiple research grants from the National Science Foundation\, the Louisiana Board of Regents and industry\, totalling over $6M. He has written several high impact publications\, including three ESI highly cited papers in two disciplines (mathematics and engineering). He also maintains several open source software projects. \nVARIATIONAL AND PHASE-FIELD MODELS OF BRITTLE FRACTURE: PAST SUCCESSES AND CURRENT ISSUES\nIn this talk Dr. Bourdin will start with a modern interpretation of Griffith’s classical criterion as a variational principle for a free discontinuity energy and will recall some of the milestones in its analysis. Then he will introduce the phase-field approximation per se and describe its numerical implementation. He will illustrate how phase-field models have led to major breakthroughs in the predictive simulation of fracture in complex situations. He will show how this applies to current issues\, including crack nucleation in nominally brittle materials\, fracture of heterogeneous materials\, and inverse problems.\n\n\n\n  \nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Applied & Interdisciplinary Mathematics program at the University of Michigan. Dr. Bourdin will be hosted by Dr. Selim Esedoglu\, Professor of Mathematics. \nThis is a hybrid event and will be held in-person and broadcasted online via Zoom. Join here.  \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-aim-seminar-blaise-bourdin-phd-professor-of-mathematics-statistics-mcmaster-university-2/
LOCATION:Zoom Event
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/07/Blaise-Bourdin.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220314T140000
DTEND;TZID=America/Detroit:20220314T150000
DTSTAMP:20260607T175150
CREATED:20220121T172527Z
LAST-MODIFIED:20230713T171008Z
UID:10000554-1647266400-1647270000@micde.umich.edu
SUMMARY:MICDE Seminar: Marta D`Elia\, Principal Member of the Technical Staff\, Sandia National Laboratories
DESCRIPTION:WATCH THE RECORDING HERE. \nBio: Marta D’Elia is a Principal Member of the Technical Staff at Sandia National Laboratories\, where she works since 2014. She’s currently part of the Data Science and Computing group at the California site. She obtained her master degree in Mathematical Engineering at Politecnico of Milano with Prof. Quarteroni and she obtained her Ph.D in Applied Mathematics at Emory University with Prof. Veneziani. There\, she worked on optimal control in CFD for cardiovascular applications. She was a postdoctoral fellow at Florida State University where she worked with Prof. Gunzburger on optimization and control for nonlocal and fractional models. She’s an associate editor of the SIAM Journal on Scientific Computing\, Advances in Continuous and Discrete Models\, Numerical Methods for PDEs\, and the Journal of Peridynamics and Nonlocal Models. Also\, she’s a co-founder of the One Nonlocal World project. Her interests include nonlocal modeling and simulation\, optimization and optimal control\, and scientific machine learning. \nScientific interests: \n\nModeling and Computational aspects of Nonlocal and Fractional equations\,\nScientific Machine Learning\,\nOptimization and Uncertainty Quantification.\n\nDATA-DRIVEN LEARNING OF NONLOCAL MODELS: BRIDGING SCALES WITH NONLOCALITY \nNonlocal models are characterized by integral operators that embed length scales in their definition. As such\, they are preferable to classical partial differential equation models in situations where the dynamics of a system is affected by the small scale behavior\, yet the small scales would require prohibitive computational cost to be treated explicitly. In this sense\, nonlocal models can be considered as coarse-grained\, homogenized models that\, without resolving the small scales\, are still able to accurately capture the system’s global behavior. However\, nonlocal models depend on “kernel functions” that are often hand tuned.\nWe propose to learn optimal kernel functions from high fidelity data by combining machine learning algorithms\, known physics\, and nonlocal theory. This combination guarantees that the resulting model is mathematically well-posed and physically consistent. Furthermore\, by learning the operator rather than a surrogate for the solution\, these models generalize well to settings that are different from the ones used during training. We apply this learning technique to find homogenized nonlocal models for subsurface solute transport solely on the basis of breakthrough curves.\nWe also apply the same kernel-learning technique to design new stable and resolution-independent deep neural networks\, referred to as Nonlocal Kernel Networks (NKN). Stability of NKNs is obtained by imposing constraints derived from the nonlocal vector calculus\, whereas deep training is performed by means of a shallow-to-deep initialization technique. We demonstrate the accuracy and stability of NKNs on PDE-learning and image-classification problems. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational modeling and machine learning are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Department of Mechanical Engineering. Dr. D`Elia will be hosted by Dr. Krishna Garikipati\, Professor of Mechanical Engineering\, and of Mathematics. \nThis is a virtual event and will be broadcasted online via Zoom. MICDE students and fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-marta-delia-phd-principal-member-of-the-technical-staff-at-sandia-national-laboratories-california/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/01/Marta-DElia.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220321T160000
DTEND;TZID=America/Detroit:20220321T170000
DTSTAMP:20260607T175150
CREATED:20210805T184953Z
LAST-MODIFIED:20230713T170841Z
UID:10000500-1647878400-1647882000@micde.umich.edu
SUMMARY:MICDE / MIDAS Seminar: Yun S. Song\, PhD\, Professor of Computer Science and Statistics\, University of California\, Berkeley
DESCRIPTION:ZOOM LINK\nBio: Professor Yun S. Song is a professor of EECS and Statistics working in mathematical and computational biology. He received his BS degrees in mathematics and physics from MIT\, and a PhD in physics from Stanford University.  Prof. Song’s research centers around computational and mathematical biology. He is generally interested in developing computational tools and statistical methods to facilitate the research of the broad biomedical community\, while also getting deeply involved in data analysis and interpretation.  Prof. Song is also interested in machine learning\, combinatorial optimization\, algorithms\, and Monte Carlo methods. \nRecent honors and awards include NIH Pathway to Independence Award K99/R00 (2006)\, Alfred P. Sloan Research Fellowship (2008)\, Packard Fellowship for Science and Engineering (2008)\, NSF CAREER Award (2009)\, Jim and Donna Gray Faculty Award for Excellence in Undergraduate Teaching (2013)\, Miller Research Professorship (2014)\, Math+X Simons Chair (2015)\, and Chan Zuckerberg Biohub Investigator Award (2017). \n\n  \nTalk Title: Mathematical and machine learning models for predicting protein synthesis and function\n  \nAbstract: Proteins are the workhorses of the cell and are involved in all aspects of cellular processes.  In spite of notable technological advances in protein biology and genomics over the past decade\, it remains an important challenge to unravel how protein synthesis and function are affected by genetic mutations.  In this talk\, I will describe our recent progress in tackling this challenge by leveraging new theoretical results on interacting particle systems and recent advances in natural language processing. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Michigan Institute for Data Science (MIDAS). Dr. Song will be hosted by Dr. George Zhang\, Professor of Ecology and Evolutionary Biology. \nThis is a hybrid event and will be held in-person and broadcast online via Zoom. Note: You may register after the event has started. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-midas-seminar-yun-s-song-phd-professor-of-computer-science-and-statistics-university-of-california-berkeley/
LOCATION:West Hall 340
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20220401T150000
DTEND;TZID=America/Detroit:20220401T160000
DTSTAMP:20260607T175150
CREATED:20220302T210252Z
LAST-MODIFIED:20230713T170700Z
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SUMMARY:MICDE/AIM Seminar: Miguel Moyers-González\, Associate Professor of Mathematics and Statistics\, University of Canterbury
DESCRIPTION:WATCH THE RECORDING HERE.\nBio: Dr. Miguel Moyers-González completed his B.Sc. at the Instituto Tecnológico Autónomo de México and his M.Sc and Ph.D. in Mathematics at the University of British Columbia. He was a postdoctoral fellow at the Université de Montréal before joining the University of Durham as a Lecturer in Applied Mathematics. He is presently an Associate Professor in the School of Mathematics & Statistics at the University of Canterbury. Dr. Moyers-Gonzalez primary research interests are in the mathematical analysis and computation of complex fluid flows. In broad terms\, the problems he has studied involve the combination of physical understanding\, i.e. of a particular application\, coupled with both theoretical and computational techniques for partial differential equations. \nInferring physical properties and topographical features from free surface flow data\nThe accurate modelling of geophysical flows often requires information that is difficult to measure and therefore poorly quantified. Such information may relate to the fluid properties or an unknown boundary condition\, for example. The premise of this talk is that when the flow is bounded by a free surface\, the deformation of this free surface contains useful information which can be used to infer such unknown quantities. The increasing availability of free surface data through remote sensing using drones and satellites provides the impetus to use mathematical methods and numerical tools to interpret the signature embedded in the free surface deformation.\nIn this talk\, we will explore the problem of recovering simultaneously the ice thickness and basal slip of an ice flow governed by the shallow ice approximation. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Applied & Interdisciplinary Mathematics program at the University of Michigan. Dr. Moyers-González will be hosted by Dr. Mariana Carrasco-Teja\, Assistant Research Scientist and Associate Director of MICDE. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nThis is a virtual event. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-aim-seminar-miguel-moyers-gonzalez-phd-associate-professor-of-mathematics-and-statistics-university-of-canterbury/
LOCATION:Your Desktop
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20220405T150000
DTEND;TZID=America/Detroit:20220405T160000
DTSTAMP:20260607T175150
CREATED:20220111T193640Z
LAST-MODIFIED:20260522T182816Z
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SUMMARY:MICDE Seminar: Douglas Spearot\, Professor of Mechanical & Aerospace Engineering\, University of Florida
DESCRIPTION:WATCH THE RECORDING HERE.\nBio: Dr. Douglas Spearot is a Newton C. Ebaugh Professor in the Department of Mechanical & Aerospace Engineering in the Herbert Wertheim College of Engineering at the University of Florida. He also holds an affiliate appointment in the Department of Materials Science & Engineering. From 2005-2015\, he was an Assistant/Associate Professor in the Department of Mechanical Engineering and a member of the Institute for Nanoscience and Engineering at the University of Arkansas. His research focuses on the use of atomistic and mesoscale simulation techniques to study the mechanical and thermodynamic properties of materials\, with particular focus on the behavior of dislocations and interfaces\, and the development of computational tools to extract experimentally relevant metrics from simulation generated data. Dr. Spearot received his B.S. in Mechanical Engineering from the University of Michigan\, and his M.S. and Ph.D. in Mechanical Engineering from the Georgia Institute of Technology. \nAwards: \n\n2010 NSF CAREER Award to elucidate the nanoscale mechanisms associated with phase selection during vapor deposition.\n2007 Ralph E. Power Junior Faculty Enhancement Award to study plasticity in nanostructured materials.\n2020 Teacher of the Year in the Department of Mechanical & Aerospace Engineering\, University of Florida.\n2014 College of Engineering Imhoff Outstanding Teaching Award\, University of Arkansas.\n2014 Arkansas Alumni Association Rising Teaching Award\, University of Arkansas.\n\nMesoscale Modeling of Plasticity in Metallic Materials via Advancement of the Discrete Dislocation Dynamics Simulation Method\nPlastic deformation in metallic materials is governed by the individual and collective behaviors of defects\, such as dislocations and grain boundaries (GBs). Among computational methods for modeling this inherently multi scale problem\, discrete dislocation dynamics (DDD) is a powerful mesoscale technique that explicitly simulates the dynamics and interactions of dislocations and provides a continuum-level understanding of plasticity. Yet\, the utility of DDD simulations for certain problems is compromised by missing defect physics and limited linkages to experiments. The focus of this seminar will be on two advancements to the DDD method. First\, a disclination-dislocation framework for modeling the mechanical structure of equilibrium GBs (EGBs) and nonequilibrium GBs (NEGBs) is incorporated into the DDD method. This approach accounts for the mechanical and kinetic effects of multiple transmission events\, and the absorption of residual dislocations at the GB. DDD simulations reveal that accumulated dislocation content from prior slip transmission lowers the external driving stresses required for subsequent slip transmission\, indicating GB softening. Second\, to enhance the connection between DDD simulations and experiments\, a new “virtual” diffraction method is developed to generate strain-broadened diffraction profiles from DDD microstructures. This method is used to generate a database of diffraction profiles from simulated dislocation microstructures\, which enables a new data-driven approach for dislocation density prediction from diffraction line profile analysis. \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in the mechanical and thermodynamic properties of materials are encouraged to attend. \nDr. Spearot will be hosted by Dr. Yue Fan\, Assistant Professor of Mechanical Engineering. \nThis is a hybrid event and will be held in-person and broadcast online via Zoom.  \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-douglas-spearot-phd-professor-of-mechanical-aerospace-engineering-university-of-florida/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20220414T103000
DTEND;TZID=America/Detroit:20220414T113000
DTSTAMP:20260607T175150
CREATED:20220322T145723Z
LAST-MODIFIED:20230713T165804Z
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SUMMARY:MICDE Seminar: Katya Scheinberg\,Professor of Operations Research and Information Engineering\, Cornell University
DESCRIPTION:WATCH THE RECORDING HERE. \nBio: Dr. Katya Scheinberg is a Professor and Director of Graduate Studies at the School of Operations Research and Information Engineering at Cornell University. Prior to joining Cornell she was the Harvey E. Wagner Endowed Chair Professor at the Industrial and Systems Engineering Department at Lehigh University. She attended Moscow University for her undergraduate studies and received her PhD degree from Columbia University. She worked at the IBM T.J. Watson Research Center as a research staff member for over a decade before joining Lehigh in 2010.\nProf. Scheinberg’s main research areas are related to developing practical algorithms (and their theoretical analysis) for various problems in continuous optimization\, such as convex optimization\, derivative free optimization\, machine learning\, quadratic programming\, etc. She is a recipient of the Lagrange Prize from SIAM and MOS\, the Farkas Prize from Informs Optimization Society and the Outstanding Simulation Publication award from Informs Simulation Society.\nProf. Scheinberg is currently the editor-in-chief of Mathematics of Operations Research\, and a co-editor of Mathematical Programming. \n\nOverview of Adaptive Optimization Methods for Stochastic Oracles\nContinuous optimization is a mature field\, which has recently undergone major expansion and change. One of the key new directions is the development of methods that do not require exact information about the objective function. Nevertheless\, the majority of these methods\, from stochastic gradient descent to “zero-th order” methods use some kind of approximate first order information. We will introduce a general definition of a stochastic and show how this definition applies in a variety of familiar settings\, including simple stochastic gradient via sampling\, traditional and randomized finite difference methods and more. We will overview several stochastic methods and how the general definition extends to the oracles used by these methods. \n  \n\nThe MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery and Engineering (MICDE) and the Department of Industrial and Operations Engineering. Dr. Scheinberg will be hosted by Dr. Albert Berahas\, Assistant Professor of Industrial and Operations Engineering. \nThis is a hybrid event and will be held in-person and broadcasted online via Zoom.  \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-katya-scheinberg/
LOCATION:1500 EECS
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20220915T150000
DTEND;TZID=America/Detroit:20220915T160000
DTSTAMP:20260607T175150
CREATED:20220818T193725Z
LAST-MODIFIED:20230713T164622Z
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SUMMARY:MICDE / IOE Seminar: Andreas Wächter\, Professor of Industrial Engineering and Management Sciences\, Northwestern University
DESCRIPTION:WATCH THE RECORDING HERE. \nAndreas Wächter is a Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. His research interests include the design\, analysis\, implementation and application of numerical algorithms for nonlinear continuous and mixed-integer optimization. He obtained his master’s degree in Mathematics at the University of Cologne\, Germany\, in 1997\, and this Ph.D. in Chemical Engineering at Carnegie Mellon University in 2002. Before joining Northwestern University in 2011\, he was a Research Staff Member in the Department of Mathematical Sciences at IBM Research in Yorktown Heights\, NY. He is a recipient of the 2011 Wilkinson Prize for Numerical Software and the 2009 Informs Computing Society Prize for his work on the open-source optimization package Ipopt. \n\n\n\nTHE ARPA-E GRID OPTIMIZATION COMPETITION\nIn recent years\, the US Advanced Research Projects Agency-Energy (ARPA-E) has been organizing the “Grid Optimization Competition.” To participate\, teams from academia and industry submitted computer program implementations of specialized algorithms for solving large realistic Security-Constrained Optimal Power Flow (SCOPF) problems. The performance of the solvers was tested and ranked independently by the organizers\, using large-scale real-life instances. The goal of SCOPF is the determination of the most cost-efficient operation of an electrical power grid in a such way that it can withstand contingencies in the form of outages of any its components. Mathematically\, this is an extremely large-scale two-stage nonlinear and nonconvex optimization problem. In this presentation\, the approach of several teams will be described\, including that of our own GO-SNIP team that placed second in the first challenge. \nFollowing the seminar IOE is holding a small reception in IOE Commons – 1709\, snacks and refreshments will be served. \n\nThe MICDE Fall 2022 Seminar Series is open to all. University of Michigan faculty and students interested in power grid optimization are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Industrial and Operations Engineering. Prof. Wächter will be hosted by Dr. Salar Fattahi\, Assistant Professor of Industrial and Operations Engineering and Dr. Siqian Shen\, Associate Professor of Industrial and Operations Engineering and Associate Professor of Civil and Environmental Engineering. \nThis event is in-person only! \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu \n 
URL:https://micde.umich.edu/event/micde-ioe-seminar-andreas-wachter-professor-of-industrial-engineering-and-management-sciences-northwestern-university/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20220923T160000
DTEND;TZID=America/Detroit:20220923T170000
DTSTAMP:20260607T175150
CREATED:20220825T193358Z
LAST-MODIFIED:20230810T200736Z
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SUMMARY:MICDE Seminar: Pania Newell\, Assistant Professor of Mechanical Engineering\, University of Utah
DESCRIPTION:Pania Newell is currently an Assistant Professor in the Department of Mechanical Engineering and holds adjunct faculty positions at the School of Computing and Civil Engineering Department at the University of Utah. Before joining The University of Utah\, she was a member of the technical staff at Sandia National Laboratories. She obtained her M.S. and Ph.D. from the University of New Mexico and the University of Colorado-Boulder\, respectively. Her research interest lies at the interface of mechanics and material sciences. In particular\, she is interested in multi-scale\, multi-physics phenomena in heterogeneous porous materials through developing theoretical\, computational\, and experimental frameworks combined with data sciences. She is the co-founder/co-host of an academic podcast called “This Academic Life”. \nMECHANICS OF HIERARCHICAL POROUS MATERIALS: DESIGN\, CONTROL AND PREDICTION \nHierarchical porous materials with pores at multiple length scales are widespread in nature. Although different compositions\, textures\, and physical properties of natural porous materials have inspired researchers and engineers to design materials with controllable pore structures\, the hierarchical structure of natural porous materials poses challenges in understanding damage and fracture in these complex systems. To be able to create nature-inspired materials\, we must have a mechanistic understanding of materials ranging from the macro- to the nanoscale. In this talk\, I will begin by providing an overview of porous materials and their substantial role in our energy sector. I will then discuss some of our recent efforts in designing nano/micro porous structures with different pore morphology and novel in-situ testing to highlight the effect of different structural and geometrical parameters in porous materials across scales. I will also show how computational tools enable us to enhance our fundamental understanding of fracture propagation mechanisms in such materials over a wide range of scales. At the nanoscale\, molecular dynamics simulation provides information about mechanical properties\, such as fracture energy release rate for various pore morphologies. At the micro-scale\, the impact of the pore shape and size on fracture pattern is investigated through a two-scale homogenization method coupled with the state-of-the-art phase-field fracture technique. The results of this hierarchical coupling approach highlight the importance of higher-order parameters associated with the pore shape and size on fracture properties and patterns at the continuum scale. \n  \n\n  \nThe MICDE Fall 2022 Seminar Series is open to all. University of Michigan faculty and students interested in multi-scale\, multi-physics phenomena in heterogeneous porous materials are encouraged to attend. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Newell will be hosted by Prof. Krishna Garikipati\, Professor of Mechanical Engineering and Mathematics and Director of MICDE. \nThis is an in-person event\, Zoom link will only be provided upon request. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu \nWATCH THE RECORDING HERE.
URL:https://micde.umich.edu/event/micde-seminar-pania-newell-assistant-professor-of-mechanical-engineering-university-of-utah/
LOCATION:1200 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20221027T153000
DTEND;TZID=America/Detroit:20221027T163000
DTSTAMP:20260607T175150
CREATED:20220825T193358Z
LAST-MODIFIED:20230713T163634Z
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SUMMARY:MICDE Seminar: John Tramm\, Assistant Computational Scientist\, Argonne National Laboratory
DESCRIPTION:Dr. John Tramm is an assistant scientist in the computational science division at Argonne National Laboratory. He received his PhD in computational nuclear engineering from MIT in 2018. John’s research efforts are focused on improving neutron transport methods that drive massively parallel simulations of nuclear reactors using some of the world’s largest supercomputers. John also has deep experience developing and optimizing simulation applications for GPU-based systems. \n  \nTHE RISE OF PORTABLE GPU PROGRAMMING: EXPERIENCES DEVELOPING GPU-BASED SCIENTIFIC SIMULATION APPLICATIONS FOR INTEL\, NVIDIA\, AND AMD GPUs \nHistorically\, portability has not been important for GPU programming as NVIDIA has dominated the high performance computing (HPC) GPU market. With only one major GPU vendor available to choose from\, it has always made sense to develop scientific HPC apps using NVIDIA’s proprietary CUDA programming model. However\, in 2022 both AMD and Intel are releasing HPC GPU products with the intention of competing directly with NVIDIA. In fact\, the world’s first exascale supercomputer (Oak Ridge National Laboratory’s Frontier) is powered by AMD GPUs\, with another even larger exascale supercomputer (Aurora) powered by Intel GPUs set to arrive at Argonne National Laboratory shortly. These new computers highlight a trend not just from CPU to GPU in HPC\, but also a trend from proprietary CUDA into a number of different portable performance models for GPU. Thus\, scientific application developers are now confronted with not only the difficultly of porting or developing apps for GPU architectures\, but also with selecting from a wide variety of portable GPU programming models (for instance\, OpenMP offloading\, HIP\, SYCL/DPC++\, OpenCL\, Kokkos\, RAJA\, and OCCA). \nIn this talk\, I will briefly introduce the newest supercomputing systems and will give an overview of the many different portable performance models now available for GPUs. I will show a few snippets of an example kernel implemented in a variety of different models\, and will even compare performance of a scientific mini-app\, XSBench\, across all major programming models and GPU architectures. Subjective “pros and cons” of each programming model will be discussed along with quantitative performance comparisons. Next\, I will use a full scientific GPU application (the OpenMC Monte Carlo particle transport code) as a case study to discuss real-world issues affecting portable scientific GPU applications and how bleeding-edge GPU compiler technology stacks are faring. I will also briefly discuss a few of the algorithmic performance optimizations that we developed for OpenMC to give a feel for what types of changes are required to achieve high performance on GPU. \n  \n\n  \nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Dr. Tramm will be hosted by Prof. Brendan Kochunas\, Assistant Professor of Nuclear Engineering and Radiological Sciences. \nThis is an in-person event\, Zoom link will only be provided upon request. This seminar will not be recorded! \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-john-tramm-assistant-computational-scientist-argonne-national-laboratory/
LOCATION:1010 H. H. Dow\, 2300 Hayward St\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20221031T153000
DTEND;TZID=America/Detroit:20221031T163000
DTSTAMP:20260607T175150
CREATED:20230714T153416Z
LAST-MODIFIED:20230714T153416Z
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SUMMARY:MICDE Seminar: Reese Jones\, Distinguished Member of the Technical Staff\, Sandia National Laboratories
DESCRIPTION:Reese Jones is currently a staff scientist at Sandia National Laboratories in Livermore\, CA. He is engaged in materials science and computational physics research with scales ranging from atomic/molecular to the continuum. He has made contributions to multi-scale methods\, electrochemical and thermal transport\, atomic-level fracture\, and contact. Recently he has been developing and applying machine learning methods to provide constitutive models for\ncomplex materials\, quantify material uncertainty\, and interpret materials imaging for reliability analysis. \nPREDICTING FAILURE IN POROUS METALS USING CONVOLUTIONAL NEURAL NETWORKS \nPredicting whether defects are critical or not is a high-value task in medicine\, materials engineering\, and other fields. Tools that augment expert opinion are needed in the current era of high resolution imaging that can reveal an overwhelming number of defects. In particular\, porosity is a persistent feature of additively manufactured materials and determines failure locations through complex mechanics that exhibit sensitivity to the initial pore locations. In the case of materials engineering expensive direct numerical simulations are available and can be used to train efficient surrogates. Neural networks\, such as the one we have developed\, enable more complete analysis of potential outcomes. \nIn this work\, we develop convolutional neural networks as surrogate models for predicting failure\nlocations. The binary classification problem of categorizing intact/failed voxels is first regularized by recasting it as a regression problem for the continuous damage field subjected to pre-processing transformations. An apparent challenge is the damage fields display a relatively small number of voxels close to failure leading to a form of class imbalance for regression that can cause the optimizer to converge to a poor local minimum. We address this through a re-weighting of the loss function which accounts for the relative frequencies of damage values. Another challenging aspect is the high sensitivity of the outcomes to the porosity field which typically creates multiple regions of high damage competing for failure. This motivates the use of Bayesian neural networks to capture sensitivities in the prediction through uncertainty quantification. We use these uncertainties to rank the likelihood of failure of any particular cluster of porosity in a reliability analysis. Lastly\, to aid transferability of the network and reduce the training burden when it is applied to new materials and processes\, we are exploring transfer learning techniques. \n  \n\n  \nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Dr. Jones will be hosted by Prof. Krishna Garikipati\, Professor of Mechanical Engineering and Mathematics and Director of MICDE. \nThis is an in-person event\, Zoom link will only be provided upon request. This seminar will not be recorded. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-reese-jones-distinguished-member-of-the-technical-staff-sandia-national-laboratories/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20221111T150000
DTEND;TZID=America/Detroit:20221111T160000
DTSTAMP:20260607T175150
CREATED:20220901T211133Z
LAST-MODIFIED:20230713T163450Z
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SUMMARY:MICDE / AIM Seminar: Jennifer Franck\, Assistant Professor of Engineering Physics\, University of Wisconsin-Madison
DESCRIPTION:WATCH THE RECORDING HERE. \nJennifer Franck is an Assistant Professor in the Department of Engineering Physics at the University of Wisconsin-Madison. She leads the Computational Flow Physics and Modeling Lab\, using computational fluid dynamics (CFD) techniques to explore the flow physics of unsteady and turbulent flows. Ongoing research projects are in the areas of bio-inspired flows and the fluid dynamics of renewable energy systems with current projects funded by NSF and ARPA-E. Prior to joining the UW-Madison faculty in 2018\, she was faculty at Brown University. She received her undergraduate degree in Aerospace Engineering from University of Virginia\, followed by a M.S. and Ph.D. from California Institute of Technology. Following her PhD\, she was awarded an NSF Postdoctoral Fellowship hosted at Brown University to computationally explore fluid dynamics mechanics of flapping flight. \nPREDICTIVE MODELING OF OSCILLATING FOIL WAKE DYNAMICS \nSwimming and flying animals rely on the fluid around them to provide lift or thrust forces\, leaving behind a distinct vortex wake in the fluid. The structure and size of the vortex wake is a blueprint of the animal’s kinematic trajectory\, holding information about the forces and also the size\, speed and direction of motion. This talk will introduce a bio-inspired oscillating turbine\, which can be operated to generate energy from moving water through lift generation\, in the same manner as flapping birds or bats. This style of turbines offers distinct benefits compared with traditional rotation-based turbines such as the ability to dynamically shift its kinematics for changing flow conditions\, thus altering its wake pattern. Current efforts lie in predicting the vortex formation and dynamics of the highly structured wake such that it can be utilized towards cooperative motion within arrays of oscillating foils. Using numerical simulations\, this talk will discuss efforts towards linking the fluid dynamic wake signature to the underlying foil kinematics\, and investigating how that effects the energy harvesting performance of downstream foils. Two machine learning methodologies are introduced to classify\, cluster and identify complex vorticity patterns and modes of energy harvesting\, and inform more detailed modeling of arrays of oscillating foils. \n  \n\nThe MICDE Fall 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational fluid dynamics are encouraged to attend. \nThis seminar is cohosted by the Michigan Institute for Computational Discovery and Engineering (MICDE) and the Applied & Interdisciplinary Mathematics program (AIM) at the University of Michigan. Prof. Franck will be hosted by Prof. Silas Alben\, Professor of Mathematics. \nThis is a virtual event broadcasted online via Zoom. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-jennifer-franck-assistant-professor-of-engineering-physics-university-of-wisconsin-madison/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20221116T153000
DTEND;TZID=America/Detroit:20221116T163000
DTSTAMP:20260607T175150
CREATED:20220825T193358Z
LAST-MODIFIED:20230713T163322Z
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SUMMARY:MICDE Seminar: Miguel Bessa Associate Professor of Engineering\, Brown University
DESCRIPTION:Miguel Bessa is an Associate Professor in the School of Engineering at Brown University. His research interests include computational mechanics and materials science\, development of numerical methods\, machine learning and optimization\, multi-scale modeling of materials and structures. Miguel Bessa and his research group envision a new era for the design of materials and structures using artificial intelligence. Miguel received a PhD in Mechanical Engineering from Northwestern University in 2016 as a Fulbright scholar. After a short postdoctoral position at Caltech (2017) and a quick leap from Assistant to Associate Professor (2021) at Delft University of Technology\, he joined the Solid Mechanics Group at Brown University in the Summer of 2022. \nCOOPERATIVE DATA-DRIVEN MODELING \nThe human brain is capable of learning tasks mostly without forgetting. However\, deep neural networks suffer from catastrophic forgetting when learning tasks one after the other. We address this challenge considering a class-incremental learning scenario where the network sees test data without knowing its origin. We show the best results to date for the ImageNet dataset\, outperforming by more than 20% the state of the art. The proposed method is also applied to learn material laws\, illustrating its versatility. This strategy is believed to open new avenues for cooperation among different researchers and practitioners. \n  \n\nThe MICDE Fall 2022 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Bessa will be hosted by Prof. Krishna Garikipati\, Professor of Mechanical Engineering and Mathematics and Director of MICDE. \nThis is an in-person event\, Zoom link will only be provided upon request. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu \nWATCH THE RECORDING HERE.
URL:https://micde.umich.edu/event/micde-seminar-miguel-bessa-associate-professor-of-engineering-brown-university/
LOCATION:1303 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20230117T140000
DTEND;TZID=America/Detroit:20230117T150000
DTSTAMP:20260607T175150
CREATED:20221222T184418Z
LAST-MODIFIED:20230714T152222Z
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SUMMARY:MICDE Seminar: Mark Vogelsberger Associate Professor of Physics\, Massachusetts Institute of Technology
DESCRIPTION:WATCH THE RECORDING HERE \nProfessor Vogelsberger grew up in Germany\, and received his undergraduate degree in physics from the University of Mainz and his Ph.D from the University of Munich and the Max Planck Institute for Astrophysics in 2010\, advised by Prof. Simon D.M. White. In 2009 he won the Rudolf Kippenhahn Prize for his thesis work. He was an ITC postdoctoral fellow at the Harvard-Smithsonian Center for Astrophysics from 2009-2012\, and a Hubble fellow from 2012-2013. In 2014\, Dr. Vogelsberger joined the MIT physics faculty as Assistant Professor. In 2016 he won an Alfred P. Sloan Fellowship in Physics. Professor Vogelsberger was promoted to Associate Professor in 2018. Professor Vogelsberger is a theoretical astrophysicist whose research interests broadly cover structure and galaxy formation\, dark matter physics and large-scale hydrodynamical simulations. He makes extensive use of numerical simulations using state-of-the-art high-performance supercomputers around the world. \n  \nSIMULATING EARLY STRUCTURE AND GALAXY FORMATION – THE THESAN PROJECT \nCosmological simulations of galaxy formation have evolved significantly over the last decade. In my talk I will describe recent efforts to model the\nlarge-scale distribution of galaxies with cosmological hydrodynamical simulations. The focus of the talk will be a discussion of our new simulation\ncampaign\, the THESAN project\, to study the epoch of re-ionization and the early Universe. \n  \n\nThe MICDE Winter 2023 Seminar Series is open to all. University of Michigan faculty and students interested in cosmology are encouraged to attend. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Vogelsberger will be hosted by Prof. Monica Valluri\, Research Professor of Astronomy. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/mark-vogelsberger-associate-professor-of-physics-massachusetts-institute-of-technology-2/
LOCATION:MI
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20230123T150000
DTEND;TZID=America/Detroit:20230123T160000
DTSTAMP:20260607T175150
CREATED:20221222T184418Z
LAST-MODIFIED:20230810T200557Z
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SUMMARY:MICDE Seminar: Albert Berahas Assistant Professor of Industrial and Operations Engineering at the University of Michigan
DESCRIPTION:Albert S. Berahas is an Assistant Professor in the Industrial and Operations Engineering department at the University of Michigan. Before joining the University of Michigan\, he was a Postdoctoral Research Fellow in the Industrial and Systems Engineering department at Lehigh University working with Professors Katya Scheinberg\, Frank Curtis and Martin Takáč. Prior to that appointment\, he was a Postdoctoral Research Fellow in the Industrial Engineering and Management Sciences department at Northwestern University working with Professor Jorge Nocedal. Berahas completed his PhD studies in the Engineering Sciences and Applied Mathematics (ESAM) department at Northwestern University in 2018\, advised by Professor Jorge Nocedal. He received his undergraduate degree in Operations Research and Industrial Engineering (ORIE) from Cornell University in 2009\, and in 2012 obtained an MS degree in Applied Mathematics from Northwestern University. Berahas’ research broadly focuses on designing\, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Specifically\, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms\, (ii) optimization algorithms for machine learning\, (iii) constrained optimization\, (iv) stochastic optimization\, (v) derivative-free optimization\, and (vi) distributed optimization. \n  \nALGORITHMS FOR DETERMINISTICALLY CONSTRAINED STOCHASTIC OPTIMIZATION \nStochastic gradient and related methods for solving stochastic optimization problems have been studied extensively in recent years. It has been shown that such algorithms and much of their convergence and complexity guarantees extend in straightforward ways when one considers problems involving simple constraints\, such as when one can perform projections onto the feasible region of the problem. However\, settings with general nonlinear constraints have received less attention\, and many of the approaches that have been proposed for solving such problems resort to using penalty or (augmented) Lagrangian methods\, which are often not the most effective strategies. In this work\, we propose and analyze stochastic optimization algorithms for deterministically constrained problems based on the sequential quadratic optimization (commonly known as SQP) methodology. We discuss the rationale behind our proposed techniques\, convergence in expectation and complexity guarantees for our algorithms\, and the results of preliminary numerical experiments that we have performed. This is joint work with Raghu Bollapragada\, Frank E. Curtis\, Michael O’Neill\, Daniel P. Robinson\, Jiahao Shi and Baoyu Zhou. \n  \n\nThe MICDE Winter 2023 Seminar Series is open to all. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Berahas will be hosted by Prof. Siqian Shen\, Associate Professor of Industrial and Operations Engineering and Associate Professor of Civil and Environmental Engineering. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/albert-berahas-assistant-professor-of-industrial-and-operations-engineering-university-of-michigan/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20230216T120000
DTEND;TZID=America/Detroit:20230216T130000
DTSTAMP:20260607T175150
CREATED:20230127T161137Z
LAST-MODIFIED:20230714T152013Z
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SUMMARY:MICDE Seminar: Mark Pauly Professor of Computer Science\, École Polytechnique Fédérale de Lausanne
DESCRIPTION:WATCH THE RECORDING HERE. \nMark Pauly is a full professor at EPFL\, where he directs the Geometric Computing Laboratory (GCM). Prior to joining EPFL\, he was assistant professor at ETH Zurich\, postdoctoral scholar at Stanford University\, and doctoral student at ETH Zurich. He received the ETH medal for outstanding dissertation in 2003\, was awarded the Eurographics Young Researcher Award in 2006\, an ERC Starting Grant in 2010\, and the Eurographics Outstanding Technical Contributions Award in 2016. He is the co-founder of two EPFL spin-offs\, Faceshift AG and Rayform SA. \n  \nCOMPUTATIONAL INVERSE DESIGN OF DEPLOYABLE STRUCTURES \nResearch at the EPFL Geometric Computing Laboratory (GCM) aims at empowering creators. We develop efficient simulation and optimization algorithms to build computational design methodologies for advanced material systems and digital fabrication technology. Mathematical reasoning\, geometric abstractions\, and powerful numerical methods are key ingredients in our work.\nIn this talk I will show how these tools can be used to solve challenging inverse problems for deployable structures that can transition between multiple geometric states. Several design studies will highlight how the interplay of geometry\, computation\, and digital fabrication technologies facilitates the discovery of new material systems with superior functional performance. Such systems offer a wide variety of potential applications\, for example in industrial and consumer products\, soft robotics\, medical devices\, or architecture. \n\n  \nThe MICDE Winter 2023 Seminar Series is open to all. University of Michigan faculty and students interested in computationally designed advanced material systems and digital fabrication technology are encouraged to attend. \nThis seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Pauly will be hosted by Prof. Evgueni Filipov\, Assistant Professor of Civil and Environmental Engineering. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/mark-pauly-professor-at-the-school-of-computer-and-communication-sciences-ecole-polytechnique-federale-de-lausanne/
LOCATION:MI
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
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