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DTSTART;TZID=America/Detroit:20200115T120000
DTEND;TZID=America/Detroit:20200115T130000
DTSTAMP:20260608T072100
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SUMMARY:MICDE Seminar: Allen Sanderson\, Research Scientist\, Scientific Computing and Imaging Institute\, The University of Utah
DESCRIPTION:Bio: Allen Sanderson\, Ph.D. is a Research Scientist at the University of Utah’s Scientific Computing and Imaging Institute. His interest lies in visualization and analysis of large data coming from application areas ranging from plasma physics to combustion. Recently he has focused on new ways to utilize in situ data analysis and visualization which often has him working directly on the science application infrastructure. \nTeasing out Ephemeral Data from HPC Applications for In Situ Visualization and Analysis\nIt is well known that as HPC applications have grown\, I/O has become a bottleneck\, which has required scientists to turn to in situ tools for data exploration. The focus of this exploration has typically been on simulation data. However\, applications also produce ephemeral data that is optionally written to disk for post hoc analysis\, but not otherwise saved or utilized by the application in subsequent time steps. One example of ephemeral data is runtime performance data. In this talk I will present the infrastructure implemented for efficiently collecting this and other data within the Uintah framework which was coupled to VisIt’s in situ toolkit for analysis and visualization. This collection and coupling allows performance data to be visualized using multiple domains giving insight previously not possible. As part this coupling\, we take advantage of VisIt’s in situ custom user interface to create a “simulation dashboard” that allows for in situ computational steering and visual debugging allowing for improvements in the development and simulation workflow. \nDr. Sanderson is being hosted by the Scientific Computing Student Club [SC2].  If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu. Limited lunch will be provided. 
URL:https://micde.umich.edu/event/micde-seminar-allen-sanderson/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200124T130000
DTEND;TZID=America/Detroit:20200124T140000
DTSTAMP:20260608T072100
CREATED:20230905T171341Z
LAST-MODIFIED:20230905T171341Z
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SUMMARY:MICDE Seminar: Andrew Wetzel\, Assistant Professor\, Physics\, University of California\, Davis
DESCRIPTION:Bio: Professor Wetzel is an assistant professor in the physics department and in the astrophysics and cosmology group at the University of California\, Davis. He is a theoretical/computational astrophysicist and cosmologist. Using the world’s most powerful supercomputers\, he generates cosmological simulations to model the formation of cosmic structures\, including galaxies and their stars. He uses these simulations as theoretical laboratories to develop and test models of galaxy formation\, stellar dynamics\, and the nature of dark matter\, with emphasis on our own Milky Way galaxy. \nSimulating the Milky Way\nThe Gaia satellite mission\, together with a multitude of ground-based observational surveys\, now measure 6-D phase-space coordinates and multi-species elemental abundances for hundreds of millions of stars across the Milky Way. This new era of galactic archeology and near-field cosmology demands a new generation of simulations that achieve high dynamic range to resolve scales of individual stellar populations within a cosmological context. I will describe the new Latte suite of massively parallelized cosmological zoom-in simulations\, run on the nation’s most powerful supercomputers\, that model the formation of Milky Way-like galaxies at parsec-scale resolution\, using the FIRE (Feedback in Realistic Environments) model for star formation and feedback. First I will discuss the formation of the Milky Way disk\, including resolving for the first time the dynamics and lifetimes of giant molecular clouds and stars clusters at z = 0. These simulations also self-consistently resolve the formation of satellite dwarf galaxies around each Milky Way-like host. These low-mass galaxies have presented significant challenges to the cold dark matter model\, but I will show progress in addressing the “missing satellites” and “too-big-to-fail” problems. Finally\, I will discuss synthetic Milky Way surveys that we have created from the Latte simulations\, which are publicly available\, to provide theoretical modeling insight for the era of Gaia. \nProf. Wetzel is being hosted by Prof. Gnedin (Astronomy).  If you would like to meet with him during his visit\, please send an email to micde-events@umich.edu. If you are an MICDE graduate student and would like to join Prof. Wetzel for lunch please RSVP by Thursday\, January 23. 
URL:https://micde.umich.edu/event/micde-seminar-andrew-wetzel-uc-davis/
LOCATION:411 West Hall (1085 S. University)\, 1085 S. University Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200131T153000
DTEND;TZID=America/Detroit:20200131T163000
DTSTAMP:20260608T072100
CREATED:20230905T171340Z
LAST-MODIFIED:20230905T171340Z
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SUMMARY:MICDE Seminar: Amir Salaree\, Postdoctoral Fellow\, Earth and Environmental Sciences\, University of Michigan
DESCRIPTION:Due to unforeseen circumstances the originally scheduled talk by Professor Brandon Johnson has been cancelled and replaced with the following seminar. \nTheoretical and Computational Contributions to the Modeling of Global Tsunamis\nThe distribution of tsunami amplitudes in the open ocean is controlled by source mechanism as well as bathymetry geometry and resolution\, with the latter controlling far-field tsunami features. However\, large detailed bathymetry grids result in long computer simulation times for tsunamis. It is therefore of interest to investigate the amount of physical detail in bathymetric grids that control the most important features in tsunami amplitudes\, to assess what constitutes sufficient level for grids in numerical simulations. By decomposing the Pacific bathymetry using a spherical harmonics approach one can create “smoothed” versions of the original field. Using these simplified bathymetries to simulate tsunamis from potential ruptures around the Pacific\, we can see that for large megathrust events (M0=1029 dyn-cm)\, only a resolution of ~1000 km (equivalent to l=40)\, or ~1% surface smoothness of the Pacific is needed in order to reproduce the main components of the true distribution of tsunami amplitudes. This would result in simpler simulations\, and faster computations in the context of tsunami warning algorithms. \nIn a separate context\, an overview of tsunami studies and a report on a study of a meteotsunami are presented. These scenarios are evidence for the fact that tsunami studies are interdisciplinary fields of research that require coordinated efforts by investigators from various backgrounds. \nMICDE is co-hosting this seminar with the Earth and Environmental Sciences department. 
URL:https://micde.umich.edu/event/micde-seminar-brandon-johnson-purdue/
LOCATION:RM1528\, 1100 North University Building
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/01/Amir-Salaree.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200221T150000
DTEND;TZID=America/Detroit:20200221T160000
DTSTAMP:20260608T072100
CREATED:20230905T171342Z
LAST-MODIFIED:20230905T171342Z
UID:10000332-1582297200-1582300800@micde.umich.edu
SUMMARY:MICDE Seminar: Osman Basaran\, Professor\, Chemical Engineering\, Purdue University
DESCRIPTION:Bio: Professor Osman Basaran is a Burton and Kathryn Gedge Professor of Chemical Engineering at Purdue University. He received his undergraduate degree at Massachusetts Institute of Technology and a PhD from the University of Minnesota. Prof. Basaran’s research involves the use of a balanced approach based on computation\, theory\, and experiment to attack a number of fundamental issues that lie at the heart of such practical problems. \nHigh-accuracy simulation of free surface flows near finite-time pinch-off and coalescence singularities\nMotivated by applications such as ink jet printing\, drop-by-drop manufacturing\, sprays\, emulsions\, and chemical separations\, we study the dynamics of breakup and coalescence through high-accuracy simulation\, theory\, and experiment.  In this talk\, I will highlight our group’s work on accurately capturing the fluid dynamics that takes place in the vicinity of finite-time singularities. The free surface flow algorithms and solvers that we develop and use rely on a sharp interface representation of phase boundaries.  In the simulations\, we are able to analyze situations that involve disparate length scales that differ by up to seven orders of magnitude (commercial codes can handle about 2-3 orders and custom codes can capture at most 3-4 orders of magnitude disparity in length scales). The primary focus of the talk will be on simulations of the breakup of surfactant-covered filaments where I will pay special attention to the pinch-off singularity.  I will also summarize some of our recent work on the pre- and post-coalescence singularities that arise when two drops or bubbles are driven together and made to merge into one.  \nThis seminar is co-sponsored by the Applied & Interdisciplinary Mathematics program. Prof. Basaran is being hosted by Prof. Deegan (Physics). If you would like to meet with Prof. Basaran during his visit\, please send an email to micde-events@umich.edu. If you are an MICDE student or an AIM student and you’re interested in having lunch with Prof. Basaran during his visit\, please RSVP by Thursday\, February 20\, 2020.
URL:https://micde.umich.edu/event/micde-seminar-osman-basaran-purdue/
LOCATION:1084 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200228T150000
DTEND;TZID=America/Detroit:20200228T160000
DTSTAMP:20260608T072100
CREATED:20230905T171341Z
LAST-MODIFIED:20230905T171341Z
UID:10000333-1582902000-1582905600@micde.umich.edu
SUMMARY:MICDE Seminar: Sarah D. Olson\, Associate Professor\, Mathematical Sciences\, Worcester Polytechnic Institute
DESCRIPTION:Bio:Sarah Olson is an Associate Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute. Olson received her undergraduate degrees in Mathematics and Biology from Providence College\, a master’s from the University of Rhode Island in Mathematics\, and a PhD in Biomathematics from North Carolina State University. She has worked in the general areas of fluid dynamics\, scientific computing\, and mathematical biology. \nSperm Navigation in Complex Environments\nMicroorganisms can swim in a variety of environments\, interacting with chemicals and other proteins in the fluid. In this talk\, we will highlight recent computational methods and results for swimming efficiency and hydrodynamic interactions of swimmers in different fluid environments. Sperm are modeled via a centerline representation where forces are solved for using elastic rod theory. The method of regularized Stokeslets is used to solve the fluid-structure interaction where emergent swimming speeds can be compared to asymptotic analysis. In the case of fluids with extra proteins or cells that may act as friction\, swimming speeds may be enhanced and attraction may not occur. \nThis seminar is co-sponsored by the Applied & Interdisciplinary Mathematics program. Prof. Olson is being hosted by Prof. Alben (MATH). If you would like to meet with her during her visit\, please send an email to micde-events@umich.edu. If you are an MICDE student or a MATH student and you would like to join Professor Olson for lunch during her visit\, please RVSP by Feb. 27. 
URL:https://micde.umich.edu/event/micde-seminar-sarah-d-olson-wpi/
LOCATION:1084 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/01/Sarah-Olson.png
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200313T150000
DTEND;TZID=America/Detroit:20200313T160000
DTSTAMP:20260608T072100
CREATED:20230905T171343Z
LAST-MODIFIED:20230905T171343Z
UID:10000009-1584111600-1584115200@micde.umich.edu
SUMMARY:MICDE Seminar: Demetrios Papageorgiou\, Professor\, Applied Mathematics\, Imperial College London
DESCRIPTION:POSTPONED UNTIL FURTHER NOTICE\nBio: Demetrious Papageorgiou is a Professor at Imperial College London.  He is an applied mathematician that works on problems that arise in fluid dynamics. He is interested in systems involving immiscible fluids that are characterized by the presence of spatiotemporally evolving sharp interfaces.  \nElectric field effects in immiscible multilayer flows\nMultilayer flows such as falling films and coating flows\, or pressure-driven flows of immiscible fluids in channels and pipes\, are fundamental in applications. Such flows are typically stable if they are slow enough (highly viscous). Such regimes arise in small-scale geometries (e.g. microfluidics)\, and electric fields can be used to drive the system out of equilibrium to produce patterning\, mixing and phase separation. \nI will begin with some experiments and direct numerical simulations (DNS) that show how electric fields can be utilized in their dual role of inducing instabilities or stability depending on geometry and orientation. I will then review the theoretical models underpinning such phenomena and will use asymptotic theories to derive and study reduced-dimension model equations that describe nonlinear interfacial waves in the presence of fields. Computations predict rich dynamics including spatiotemporal chaos and singularity formation. Some novel inertialess nonlinear interfacial instabilities will also be described – these arise due to flux functions of derived evolution equations changing type from hyperbolic to elliptic. Finally\, I will present results on the use of electric fields and/or blowing suction in achieving feedback and optimal control of falling film flows. Comparisons with DNS will be made and these will be used beyond the range of validity of asymptotic models to predict phenomena such as electrostatic suppression of Rayleigh-Taylor instabilities\, and electrostatically induced pumping in microchannels. \nThis seminar is co-sponsored by the Applied & Interdisciplinary Mathematics program. Prof. Papageorgiou is being hosted by Prof. Krasny (MATH).
URL:https://micde.umich.edu/event/fall2019-papageorgiou-imperialcollege/
LOCATION:MI
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/portrait.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200313T150000
DTEND;TZID=America/Detroit:20200313T160000
DTSTAMP:20260608T072100
CREATED:20230905T171343Z
LAST-MODIFIED:20230905T171343Z
UID:10000355-1584111600-1584115200@micde.umich.edu
SUMMARY:CANCELLED - MICDE/AIM Seminar: Lyudmyla Barannyk\, Associate Professor\, Mathematics\, University of Idaho
DESCRIPTION:Bio: Lyudmyla Barannyk is an Associate Professor in the Department of Mathematics at the University of Idaho. Barannyk received a masters in Applied Mathematics from the New Jersey Institute of Technology and a PhD in Mathematics Sciences from the New Jersey Institute of Technology and Rutgers the State University of New Jersery. She is currently a visiting Associate Professor of Mathematics at the University of Michigan. \nModeling of the solid-liquid phase change in materials with internal heat generation\nWe study a simple model for the evolution of the solid-liquid interface during melting and solidification (Stefan problem) of a material with constant internal heat generation and prescribed heat flux at the boundary in the cylindrical geometry. The problem is motivated by the need to control the behavior of nuclear fuel rods in a potential meltdown scenario. The equations are solved by splitting them into transient and steady-state components and then using separation of variables. This results in an ordinary differential equation for the interface that involves infinite series. The initial value problem is solved numerically\, and solutions are compared to the previously published quasi-static solutions. We show that when the internal heat generation and boundary heat flux are close in value\, the motion of the phase change front takes longer to reach steady-state than when the values are farther apart. As the difference between the internal heat generation and boundary heat flux increases\, the transient solutions become more dominant and the phase change front does not reach steady-state before the outer boundary or centerline is reached. Hence the difference between the internal heat generation and boundary heat flux can be used to control the motion and speed of the solid-liquid interface. Limitations of the present model and possible future extensions will be discussed. \n\n\n\nThis is joint work with Sidney Williams (Georgia Tech)\, Irene Ogidan (University of Idaho)\, John Crepeau (University of Idaho)\, and Alexey Sakhnov (Kutateladze Institute of Thermophysics\, Novosibirsk\, Russia).
URL:https://micde.umich.edu/event/micde-aim-seminar-lyudmyla-barannyk/
LOCATION:MI
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/03/Lyudmyla-Barannyk.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200320T150000
DTEND;TZID=America/Detroit:20200320T160000
DTSTAMP:20260608T072100
CREATED:20230905T171343Z
LAST-MODIFIED:20230905T171343Z
UID:10000351-1584716400-1584720000@micde.umich.edu
SUMMARY:POSTPONED - MICDE/AIM Seminar: John Harlim\, Professor\, Mathematics and Meteorology\, Penn State University
DESCRIPTION:Bio: John Harlim is a Professor in the Department of Mathematics and the Department of Meteorology and Atmospheric Sciences. Harlim received his undergraduate degree in Mathematics from the Universitas Padjadaran (Indonesia)\, a master’s from the University of Guelph in Applied Mathematics\, and a PhD in Applied Mathematics and Scientific Computation from the University of Maryland at College Park. His research interests in applied mathematics include parameter estimation\, machine learning\, manifold learning\, operator estimation\, data assimilation. \n Learning Missing Dynamics through Data\nThe recent success of machine learning has drawn tremendous interest in applied mathematics and scientific computations. In this talk\, I would address the classical closure problem that is also known as model error\, missing dynamics\, or reduced-order-modeling in various community. Particularly\, I will discuss a general framework to compensate for the model error. The proposed framework reformulates the model error problem into a supervised learning task to approximate a very high-dimensional target function involving the Mori-Zwanzig representation of projected dynamical systems. Connection to traditional parametric approaches will be clarified as specifying the appropriate hypothesis space for the target function. Theoretical convergence and numerical demonstration on modeling problems arising from PDE’s will be discussed.
URL:https://micde.umich.edu/event/micde-seminar-john-harlim-psu/
LOCATION:MI
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/03/John-Harlim.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200326T160000
DTEND;TZID=America/Detroit:20200326T170000
DTSTAMP:20260608T072100
CREATED:20230905T171342Z
LAST-MODIFIED:20230905T171342Z
UID:10000352-1585238400-1585242000@micde.umich.edu
SUMMARY:POSTPONED - MICDE/EEB Seminar: Yun Song\, Professor\, Computer Science and Statistics\, University of California\, Berkeley
DESCRIPTION:Bio: Yun S. Song is a professor of EECS and Statistics. He received the BS degrees in mathematics and physics from MIT\, and a PhD in physics from Stanford University. After his PhD\, he spent a year at the Mathematical Institute at the University of Oxford\, where he decided to change fields. He became a postdoctoral researcher in the Department of Statistics at Oxford\, and started doing research in computational biology and mathematical population genetics. From 2004 to 2007\, he was a postdoctoral researcher at UC Davis in the Department of Computer Science\, and the Section of Evolution and Ecology. \nThe key parameters that govern translation efficiency\nTranslation of mRNA into protein is a fundamental biological process mediated by the flow of ribosomes on mRNA transcripts.  With multiple factors that can potentially affect its efficiency\, this transport process is highly complex and heterogeneous: different mRNAs can have different initiation rates\, local elongation rates can vary substantially along the mRNA\, and multiple ribosomes can simultaneously translate the same mRNA\, potentially leading to interference.  In this talk\, I will present new theoretical results on a probabilistic model of mRNA translation which allowed us to identify the key parameters that govern the overall rate of protein synthesis\, sensitivity to initiation rate changes\, and efficiency of ribosome usage.  I will then describe our ongoing study\, which combines in vitro translation experiments with mathematical modeling\, to elucidate the role of the 5′ UTR (particularly uAUGs and uORFs) in regulating translation initiation in eukaryotes.
URL:https://micde.umich.edu/event/micde-seminar-yun-song/
LOCATION:MI
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2021/08/Yun-S.-Song.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200417T150000
DTEND;TZID=America/Detroit:20200417T163000
DTSTAMP:20260608T072100
CREATED:20230905T171344Z
LAST-MODIFIED:20230905T171344Z
UID:10000606-1587135600-1587141000@micde.umich.edu
SUMMARY:Webinar: Transmission modeling of infectious diseases and the COVID-19 outbreak
DESCRIPTION:This seminar will focus on differential equation transmission modeling approaches to analyze the spread of infections diseases\, and how Prof. Eisenberg and her colleagues are using them to model the current COVID-19 outbreak in the State of Michigan.Their current model is helping to forecast the numbers of laboratory-confirmed cases\, fatalities\, hospitalized patients\, and hospital capacity issues (such as ICU beds needed)\, and examining how social distancing can impact the spread of the epidemic.
URL:https://micde.umich.edu/event/webinar-transmission-modeling-of-infectious-diseases-and-the-covid-19-outbreak/
LOCATION:BlueJeans Events
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Webinar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/07/Marisa-Eisenberg.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200602T150000
DTEND;TZID=America/Detroit:20200602T160000
DTSTAMP:20260608T072100
CREATED:20230905T171345Z
LAST-MODIFIED:20230905T171345Z
UID:10000605-1591110000-1591113600@micde.umich.edu
SUMMARY:MICDE Webinar Series: Gabriel Ehrlich\, Director\, Research Seminar in Quantitative Economics\, University of Michigan
DESCRIPTION:Bio: Dr. Gabriel Ehrlich is the Director of the Research Seminar in Quantitative Economics (RSQE)\, and an Assistant Research Scientist in the department of Economics at the University of Michigan. Prior to joining RSQE\, he worked in the Financial Analysis Division at the Congressional Budget Office (CBO)\, where he forecast interest rates and conducted analysis on monetary policy and the mortgage finance system. His academic research focuses on several areas of housing and land economics as well as the effects of wage rigidity on labor market outcomes. \nMODELING THE ECONOMIC OUTLOOK IN THE TIME OF COVID-19\nWe will present the Research Seminar in Quantitative Economics’ (RSQE’s) forecast for the national and Michigan economies from 2020 to 2022. We will discuss the incoming data during the COVID-19 pandemic\, the near-term economic damage\, and the prospects for economic recovery. RSQE is the world’s oldest continuously operating economic forecasting unit and is home to the “Michigan Model” of the U.S. economy.
URL:https://micde.umich.edu/event/micde-webinar-series-gabriel-ehrlich-director-research-seminar-in-quantitative-economics-university-of-michigan/
LOCATION:Zoom Event\, MI\, United States
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2023/07/Gabriel-Ehrlich.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20200929T140000
DTEND;TZID=America/Detroit:20200929T150000
DTSTAMP:20260608T072100
CREATED:20230905T171252Z
LAST-MODIFIED:20230905T171252Z
UID:10000409-1601388000-1601391600@micde.umich.edu
SUMMARY:MICDE / Mechanical Engineering Seminar: Sophia Haussener\, Associate Professor\, Laboratory of Renewable Energy Science and Engineering\, EPFL\, Lausanne\, Switzerland
DESCRIPTION:View webinar recording. \nBio: Sophia Haussener is an Associate Professor heading the Laboratory of Renewable Energy Science and Engineering at the Ecole Polytechnique Fédérale de Lausanne (EPFL). Her current research is focused on providing design guidelines for thermal\, thermochemical\, and photoelectrochemical energy conversion reactors through multi-physics modelling. Her research interests include: thermal sciences\, fluid dynamics\, charge transfer\, electro-magnetism\, and thermo/electro/photochemistry in complex multi-phase media on multiple scales. She received her MSc (2007) and PhD (2010) in Mechanical Engineering from ETH Zurich. Between 2011 and 2012\, she was a postdoctoral researcher at the Joint Center of Artificial Photosynthesis (JCAP) and the Energy Environmental Technology Division of the Lawrence Berkeley National Laboratory (LBNL). She has published over 70 articles in peer-reviewed journals and conference proceedings\, and 2 books. She has been awarded the ETH medal (2011)\, the Dimitris N. Chorafas Foundation award (2011)\, the ABB Forschungspreis (2012)\, the Prix Zonta (2015)\, the Global Change Award (2017)\, and the Viskanta Award (2019)\, and is a recipient of a Starting Grant of the Swiss National Science Foundation (2014). She is a deputy leader in the Swiss Competence Center for Energy Research (SCCER) on energy storage and acts as a Member of the Scientific Advisory Council of the Helmholtz Zentrum. \nModelling\, experimentation and scaling of photo-electrochemical fuel processing devices\nThe development of a sustainable energy economy based on renewable\, carbon-neutral energy is a necessary and urgent task. Photo-electrochemical approaches for solar fuels and materials are interesting\, provided they can be efficiently\, stably\, scalably\, and sustainably implemented. The functionality of such devices relies on complicated and coupled multi-physics processes\, occurring at multiple temporal and spatial scales. Device modelling can actively and efficiently support the choice of the most promising – in terms of efficiency\, cost\, robustness\, scalability\, and practicability – conceptual design pathways\, material choices\, and operating approaches. \nFirst\, I focus on cost competitive photo-electrochemical (PEC) devices identified through quasi-transient techno-economic modelling [1]. I will describe the conceptual idea of thermal integration in the context of PEC [2]\, provide results of maximum theoretical efficiency calculations to quantify the benefits\, and review the modelling framework that enabled the design of a feasible device [3]. I will illustrate how we have used our models to design and implement a PEC device with a solar-to-fuel efficiency of 17%\, and discuss ongoing approaches to scale up by our lab in order to bridge the gap between research and practical applications. \nSecond\, I will discuss detailed multi-dimensional mesoscale models that allow to assess the transport in complex (photo)electrodes. Specifically\, we use direct pore-level simulations for the coupled transport characterization of mesostructured (photo)electrodes utilizing nano-tomography techniques to obtain the exact mesostructure that is utilized in direct numerical simulations [4]. I will extend these investigations to ordered structures for the assessment of the transport in mesostructured electrodes for the electorchemical reduction of CO2 and discuss the effect of the mass transport on selectivity and activity [5]. I will then present possibilities to simplify these involved multi-dimensional numerical models into rapid screening models based on semi-analytical correlations. I will discuss analysis results for a large range of semiconductor materials [6\,7]. I will end with an outlook on research challenges and gaps in the field of (photo)electrochemical water and CO2 splitting. \n\nThis seminar is co-hosted by the Michigan Institute for Computational Discovery & Engineering\, and the Mechanical Engineering department within the University of Michigan College of Engineering. Dr. Haussener will be hosted by Rohini Bala Chandran\, Assistant Professor of Mechanical Engineering. \nThe MICDE Fall 2020 and Winter 2021 Seminar Series is open to the general public. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.  \nQuestions? Email MICDE-events@umich.edu \n\nReferences: \n[1] M. Dumortier\, S. Tembhurne\, S. Haussener\, Energy Environ. Sci. \, 8:3614–3628\, 2015\n[2] S. Tembhurne\, F. Nandjou\, S. Haussener\, Nature Energy\, 10.1038/s41560-019-0373-7\, 2019\n[3] S. Tembhurne\, S. Haussener\, Journal of The Electrochemical Society \, 163:H1008-H1018\, 2016\n[4] S. Suter\, M. Catoni\, Y. Gaudy\, S. Pokrant\, S. Haussener\, Linking Morphology and Multi-Physical Transport in\nStructured Photoelectrodes\, Sustainable Energy & Fuels \, doi: 10.1039/C8SE00215K\, 2018.\n[5] S. Suter\, S. Haussener\, Energy Environmental Science \, doi: 10.1039/C9EE00656G\, 2019.\n[6] Y. Gaudy\, S. Haussener\, Rapid Performance Optimization Method for Photoelectrodes\, Journal of Physical Chemistry\nC\, doi: 10.1021/acs.jpcc.9b04102\, 2019.\n[7] Y. Gaudy\, Z. Gacevic\, Haussener\, Theoretical maximum photogeneration efficiency and performance characterization\nof InxGa1-xN/Si tandem water-splitting photoelectrodes\, APL Materials\, accepted\, 2020.
URL:https://micde.umich.edu/event/micde-mechanical-engineering-seminar-sophia-haussener-associate-professor-laboratory-of-renewable-energy-science-and-engineering-swiss-federal-institute-of-technology-lausanne/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Sophia-Haussener.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201020T113000
DTEND;TZID=America/Detroit:20201020T130000
DTSTAMP:20260608T072100
CREATED:20230905T171253Z
LAST-MODIFIED:20230905T171253Z
UID:10000405-1603193400-1603198800@micde.umich.edu
SUMMARY:LSA Complex Systems / MICDE / MIDAS Seminar: Marissa Renardy\, Research Fellow\, Microbiology & Immunology\, University of Michigan
DESCRIPTION:Predicting the second wave of COVID-19 in Washtenaw County\, MI\nAbstract: In this work\, we study and predict the spread of COVID-19 in Washtenaw County\, MI through applying a discrete and stochastic network-based modeling framework. In this framework\, we construct contact networks based on synthetic population datasets specific for Washtenaw County that are derived from US Census datasets. We assign individuals to households\, workplaces\, schools\, and group quarters (such as prisons or long term care facilities). In addition\, we assign casual contacts to each individual at random. Using this framework\, we explicitly simulate Michigan-specific government-mandated workplace and school closures as well as social distancing measures. We perform sensitivity analyses to identify key model parameters and mechanisms contributing to the observed disease burden in the three months following the first observed cases of COVID-19 in Michigan. We then consider several scenarios for relaxing restrictions and reopening workplaces to predict what actions would be most prudent. In particular\, we consider the effects of 1) different timings for reopening\, and 2) different levels of workplace vs. casual contact re-engagement. Through simulations and sensitivity analyses\, we explore mechanisms driving the magnitude and timing of a second wave of infections upon re-opening. \nThis work is based on Dr. Renardy’s paper in press in the Journal of Theoretical Biology with coauthors:\nMarisa Eisenberg\, UM Complex Systems & Math (LSA) and Epidemiology (Public Health)\nDenise Kirschner\, UM Department of Microbiology & Immunology (Medical School) \nRegistration is not required for this event\, you may join the seminar via this link. \nThe recording of this webinar will be available for viewing soon! \nThis seminar is hosted by the LSA Center for the Study of Complex Systems\, and co-sponsored by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Michigan Institute for Data Science (MIDAS).
URL:https://micde.umich.edu/event/lsa-complex-systems-micde-midas-seminar-marissa-renardy-research-fellow-microbiology-immunology-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/Marissa-Renardy.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201020T150000
DTEND;TZID=America/Detroit:20201020T160000
DTSTAMP:20260608T072100
CREATED:20230905T171253Z
LAST-MODIFIED:20230905T171253Z
UID:10000402-1603206000-1603209600@micde.umich.edu
SUMMARY:MICDE Seminar: Grace Gu\, Assistant Professor\, Mechanical Engineering\, University of California- Berkeley
DESCRIPTION:About Grace Gu: Grace X. Gu is an Assistant Professor of Mechanical Engineering at the University of California\, Berkeley. She received her Ph.D. and MS in Mechanical Engineering from the Massachusetts Institute of Technology and her BS in Mechanical Engineering from the University of Michigan\, Ann Arbor. Her current research focuses on creating new materials with superior properties for mechanical\, biological\, and energy applications using multiphysics modeling\, artificial intelligence\, and high-throughput computing\, as well as developing intelligent additive manufacturing technologies to realize complex material designs previously impossible. Gu is the recipient of several awards\, including the 3M Non-Tenured Faculty Award\, MIT Tech Review Innovators Under 35\, Johnson & Johnson Women in STEM2D Scholars Award\, Royal Society of Chemistry Materials Horizons Outstanding Paper Prize\, and SME Outstanding Young Manufacturing Engineer Award. \n  \n\nMETAMATERIALS DESIGN AND MANUFACTURING: LEARNING FROM BIOLOGY AND ARTIFICIAL INTELLIGENCE\nAfter billions of years of evolution\, it is no surprise that biological materials are treated as an invaluable source of inspiration in the search for new materials. Additionally\, developments in computation spurred the fourth paradigm of materials discovery and design using artificial intelligence. Our research aims to advance design and manufacturing processes to create the next generation of high-performance engineering and biological materials by harnessing techniques integrating artificial intelligence\, multiphysics modeling\, and multiscale experimental characterization. This work combines computational methods and algorithms to investigate design principles and mechanisms embedded in materials with superior properties\, including bioinspired materials. Additionally\, we develop and implement deep learning algorithms to detect and resolve problems in current additive manufacturing technologies\, allowing for automated quality assessment and the creation of functional and reliable structural materials. These advances will find applications in robotic devices\, energy storage technologies\, orthopedic implants\, among many others. In the future\, this algorithmically driven approach will enable materials-by-design of complex architectures\, opening up new avenues of research on advanced materials with specific functions and desired properties. \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. \nTo view the recording for this event\, please complete this form and a link will be sent to you. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-grace-gu-assistant-professor-mechanical-engineering-university-of-california-berkeley/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Grace-Gu.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201112T110000
DTEND;TZID=America/Detroit:20201112T120000
DTSTAMP:20260608T072100
CREATED:20230905T171255Z
LAST-MODIFIED:20230905T171255Z
UID:10000403-1605178800-1605182400@micde.umich.edu
SUMMARY:MICDE Seminar: Denise Kirschner\, Professor\, Microbiology and Immunology\, University of Michigan Medical School
DESCRIPTION:About Denise Kirschner: Dr. Kirschner received her Bachelors\, Masters and PhD in applied mathematics from Tulane University. She did graduate work also at Los Alamos National Labs and a postdoctoral fellowship at Vanderbilt University joint with the departments of Mathematics and Infectious Diseases. Over the past 25 years Dr. Kirschner has focused on questions related to models of host-pathogen interactions in infectious diseases. Her main focus has been to build models of persistent infections (e.g. Helicobacter pylori and Mycobacterium tuberculosis and HIV-1). Her goal is to understand the complex dynamics involved\, together with how perturbations to this interaction (via treatment with chemotherapies or immunotherapies) can lead to prolonged or permanent health. For the past 20 years\, her research focus has been on building multi-scale models to describe the host immune response to M. tuberculosis at multiple spatial and time scales and in multiple physiological compartments including lung\, lymph nodes and blood. \nTo date\, she have worked and collaborated with experimentalists generating data on TB with mouse\, non-human primate and human studies. Denise has over 150 publications in top journals describing this work that spans topics from methodological to biological advancement. Dr. Kirschner currently serves (and has for the past 17 years) as Editor-in-Chief of the Journal of Theoretical Biology. She serves as the founding co-director of The Center for Systems Biology at the University of Michigan\, an interdisciplinary center at the University of Michigan aimed to facilitate research and training between wet-lab and theoretical scientists. In 2016 she was elected as President-elect of the Society for Mathematical Biology and has served as its president from 2017-2020. Denise’s passion for mentoring students\, postdoctoral fellows and junior faculty has been a major focus of her career\, and her key mission is to promote both mathematics and family values in the scientific community.\n \nAPPROACHES FOR STUDYING MULTISCALE COMPUTATIONAL MODELS:  \nMycobacterium tuberculosis is a bacterium that infects 1/3 of the world today. While only 10% of infected individuals experience active tuberculosis disease\, if left untreated infection results in death. The remainder of individuals harbor the bacteria in a clinically latent infection\, and those individuals can experience reactivation of infection up to 10% per year. Our goal in a number of studies is to understand the role of the bacteria in initiating\, sustaining and inhibiting the immune response during infection. Granulomas are a hallmark of tuberculosis infection arising within lungs of infected humans. Understanding the immune response that leads to formation of granulomas can help us better design therapies to control or clear infection. We use a hybrid multi-scale approach that is fine grained for spatial details to help uncover these dynamics paired with a coarse grained spatial model that allows us to capture the entire host dynamics. We use a combination of statistic and mathematical and engineering approaches to predict optimal treatments. \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 full webinar here. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-denise-kirschner-professor-microbiology-and-immunology-university-of-michigan-medical-school/
LOCATION:Zoom Event
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2020/09/Denise-Kirschner.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20201120T150000
DTEND;TZID=America/Detroit:20201120T160000
DTSTAMP:20260608T072100
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:20260608T072100
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210119T160000
DTEND;TZID=America/Detroit:20210119T170000
DTSTAMP:20260608T072100
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:20260608T072100
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:20260608T072100
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:20260608T072100
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210216T150000
DTEND;TZID=America/Detroit:20210216T160000
DTSTAMP:20260608T072100
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:20260608T072100
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210311T140000
DTEND;TZID=America/Detroit:20210311T150000
DTSTAMP:20260608T072100
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210318T110000
DTEND;TZID=America/Detroit:20210318T120000
DTSTAMP:20260608T072100
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
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20210319T150000
DTEND;TZID=America/Detroit:20210319T160000
DTSTAMP:20260608T072100
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:20260608T072100
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:20260608T072100
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:20260608T072100
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20211208T160000
DTEND;TZID=America/Detroit:20211208T170000
DTSTAMP:20260608T072100
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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END:VCALENDAR