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DTSTART;TZID=America/Detroit:20171211T100000
DTEND;TZID=America/Detroit:20171211T110000
DTSTAMP:20260606T231420
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
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SUMMARY:ME Faculty Candidate Seminar Series:  Xiu Yang\, Computational Mathematics Scientist\, Pacific Northwest National Laboratory
DESCRIPTION:Bio: Dr. Xiu Yang received his B.S. and M.Sc. in computational mathematics from Peking University\, Beijing\, China and Ph.D. in applied mathematics from Brown University\, Providence\, RI. He is currently a research scientist in computational mathematics group at Pacific Northwest National Laboratory\, Richland\, WA. His research interests include uncertainty quantification\, multi-scale modeling\, multi-fidelity data fusion and inverse problem.\n  \n Uncertainty Quantification for Complex Systems Using Limited Data\nRealistic analysis and design of complex engineering systems require not only a fine understanding of the underlying physics\, but also a significant recognition of uncertainties and their influences on the quantities of interest. Intrinsic variabilities and lack of knowledge about system parameters or governing physical models often considerably affect quantities of interest and decision-making processes. For complex systems\, the available data for quantifying uncertainties or analyzing sensitivities are usually limited because the cost of conducting a large number of experiments or running many large-scale simulations can be prohibitive. Efficient approaches of representing uncertainties using limited data are critical for such problems. I will talk about three methods for uncertainty quantification by constructing surrogate model of the quantity of interest. The first method is the adaptive functional ANOVA method\, which constructs the surrogate model hierarchically by analyzing the sensitivities of individual parameters. The second method is the sparse regression based on identification of low-dimensional structure\, which exploits low-dimensional structures in the parameter space and solves an optimization problem to construct the surrogate model. The third one is the multi-fidelity information fusion via Gaussian process regression\, which integrates limited high-fidelity data with a large number of low-fidelity data. I will demonstrate the efficiency of these methods in applications including perturbation of drag and lift in aerodynamics\, solvation energy computing in chemical biology\, stability analysis of power grid system and optimizing Li-O2 battery design. \n  \n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-xiu-yang-computational-mathematics-scientist-pacific-northwest-national-laboratory/
LOCATION:2150 H.H. Dow\, 2300 Hayward St\, Ann Arbor\, 48109\, United States
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171206T120000
DTEND;TZID=America/Detroit:20171206T130000
DTSTAMP:20260606T231420
CREATED:20230905T171416Z
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SUMMARY:ME Faculty Candidate Seminar Series: Xun (Ryan) Huan\, Combustion Research Facility at Sandia National Laboratories
DESCRIPTION:Bio: Xun (Ryan) Huan is a postdoctoral researcher in the Combustion Research Facility at Sandia National Laboratories. He received a Ph.D. in Computational Science and Engineering from MIT Department of Aeronautics and Astronautics. He also has a master’s degree from MIT and a bachelor’s degree from the University of Toronto\, both in Aerospace Engineering. Xun’s research interests broadly revolve around uncertainty quantification\, decision-making under uncertainty\, data-driven modeling\, and optimization for engineering applications. Outside work\, he is an ice hockey player and a pilot. \nFinding the Most Informative Data Using Model-based Optimal Experimental Design\nExperimental data play a crucial role in developing and refining models of physical systems. However\, some experiments produce more useful data than others\, and well-chosen experiments can provide substantial resource savings. Optimal experimental design (OED) thus seeks to systematically quantify and maximize the value of experiments. We introduce general mathematical frameworks and algorithmic approaches for OED with nonlinear models. The formalism employs Bayesian statistics and an information-theoretic objective\, and rigorously defines the conditions under which batch experiments (experiments chosen simultaneously) and sequential experiments (forward-looking designs with data feedback) are truly optimal. Finding these optimal designs using conventional means is generally intractable. We develop practical numerical methods for OED by advancing computational techniques on several fronts\, including stochastic optimization\, polynomial chaos surrogate modeling\, approximate dynamic programming\, and transport maps. Using the overall algorithm\, we design combustion experiments for optimal learning of Arrhenius kinetic parameters\, and sequential sensor placement for contaminant source inversion.\n\n* Lunch won’t be provided but you are welcome to bring your own\n\n\n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-xun-ryan-huan/
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171205T150000
DTEND;TZID=America/Detroit:20171205T160000
DTSTAMP:20260606T231420
CREATED:20230905T171416Z
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SUMMARY:MICDE Seminar: Tarek Zohdi\, Department of Mechanical Engineering\, University of California\, Berkeley
DESCRIPTION:Bio: Tarek I. Zohdi received his Ph.D. in 1997 in Computational and Applied Mathematics from the University of Texas at Austin and his Habilitation in General Mechanics from the Gottfried Leibniz University of Hannover in 2002. He is currently a Chancellor’s Professor of Mechanical Engineering\, Chair of the Computational and Data Science and Engineering Program at UC Berkeley and holder of the W. C. Hall Family Endowed Chair in Engineering. He also holds a Staff Scientist position at Lawrence Berkeley National Labs. His main research interests are in computational approaches for advanced manufacturing and nonconvex multiscale-multiphysics inverse problems\, in particular addressing the issue of how large numbers of micro-constituents interact to produce macroscale aggregate material behavior. He has published over 145 archival refereed journal papers and five books. In 2000\, he received the Zienkiewicz Prize and Medal\, which are awarded once every two years\, to one post-graduate researcher under the age of 35\, by The Institution of Civil Engineers in London\, to commemorate the work of Professor O. C. Zienkiewicz\, for research which contributes most to the field of numerical methods in engineering. In 2002\, he received the Best Paper of the Year 2001 Award in London\, at the Lord’s Cricket Grounds\, for a paper published in Engineering Computations\, pertaining to modeling and simulation of the propagation of failure in particulate aggregates of material. In 2003\, he received the Junior Achievement Award of the American Academy of Mechanics. The award is given once a year\, to one post-graduate researcher\, to recognize outstanding research during the first decade of a professional career. In 2008\, he was elected Fellow of the International Association for Computational Mechanics (IACM) and in 2009 he was elected Fellow of the United Stated Association for Computational Mechanics (USACM). He was elected President of the USACM in 2012\, and served from 2012 to 2014. He is an editor of Computational Mechanics\, Editor in Chief of Computational Particle Mechanics and serves on 12 editorial boards of international journals. For more information visit http://www.me.berkeley.edu/people/faculty/tarek-i-zohdi \nModeling and Simulation of Multistage Multiphysical Processes in Next-Generation Advanced Manufacturing and 3D Printing with New Multifunctional Materials\nWithin the last decade\, several industrialized countries have stressed the importance of advanced manufacturing to their economies. Many of these plans have highlighted the development of additive manufacturing techniques\, such as 3D printing\, which are still in their infancy. The objective is to develop superior products\, produced at lower overall operational costs. For these   goals to be realized\, a deep understanding of the essential ingredients comprising the materials involved in additive manufacturing is needed. The combination of rigorous material modeling theories\, coupled with the dramatic increase of computational power can potentially play a significant role in the analysis\, control\, and design of many emerging additive manufacturing processes. Specialized materials and the precise   design of their properties are key factors in the processes. Specifically\, particle-functionalized materials play a central role in this field\, in three main ways:   (1) to endow filament-based materials by adding particles to a heated binder   (2) to “functionalize” inks by adding particles to freely flowing solvents and (3) to directly deposit particles\, as dry powders\, onto surfaces and then to heat them with a laser\, e-beam or other external source\, in order to fuse them into place. The goal of these processes is primarily to build surface structures\, coatings\, etc.\, which are extremely difficult to construct using classical manufacturing methods. The objective of this presentation is to introduce the audience to basic techniques which can allow them to rapidly develop and analyze particulate-based materials needed in new additive manufacturing processes. This presentation is broken into two main parts: continuum and discrete element approaches. The materials associated with methods (1) and (2) are closely related types of continua (particles embedded in a continuous binder) and are treated using continuum approaches. The materials in method (3)\, which are of a discrete particulate character\, are analyzed using discrete element methods. \nProf. Zohdi is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet with him please email mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-tarek-zohdi-department-of-mechanical-engineering-university-of-california-berkeley/
LOCATION:1109 FXB\, 1320 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:20171204T100000
DTEND;TZID=America/Detroit:20171204T110000
DTSTAMP:20260606T231420
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
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SUMMARY:ME Faculty Candidate Seminar Series: Maziar Raissi\, Brown University
DESCRIPTION:Bio: Maziar Raissi is an Assistant Professor of Applied Mathematics (research) in the Division of Applied Mathematics at Brown University. He received his Ph.D. in Applied Mathematics & Statistics\, and Scientific Computations from University of Maryland — College Park in December 2016. Raissi’s expertise lies at the intersection of Probabilistic Machine Leaning\, Deep Learning\, and Data Drive Scientific Computing. \nHidden Physics Models: Machine Learning of Non-linear Partial Differential Equations\n\nA grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws\, physical principles\, and/or phenomenological behaviours expressed by differential equations with the vast data sets available in many fields of engineering\, science\, and technology. At the intersection of probabilistic machine learning\, deep learning\, and scientific computations\, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision\, this work is exploring two complimentary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics\, expressed by time dependent and non-linear differential equations\, to extract patterns from high-dimensional data generated from experiments\, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data\, infer latent quantities of interest (e.g.\, the solution to a differential equation)\, and naturally quantify uncertainty in computations. The latter is aligned in spirit with the emerging field of probabilistic numerics. \n\n\nThis is a talk of potential interest to the MICDE community. The speakers in this seminar series are Faculty Candidates in the department of Mechanical Engineering for a Computational Science search that is being carried out with the active engagement of MICDE. We expect that the successful candidate will be a highly engaged affiliate of MICDE.
URL:https://micde.umich.edu/event/me-faculty-candidate-seminar-series-maziar-raissi-brown-university/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2023/02/MaziarRaissi.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171201T180000
DTEND;TZID=America/Detroit:20171201T190000
DTSTAMP:20260606T231420
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
UID:10000114-1512151200-1512154800@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop/2017-12-01/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171201T083000
DTEND;TZID=America/Detroit:20171201T173000
DTSTAMP:20260606T231420
CREATED:20230905T171416Z
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SUMMARY:2017 U-M Data Science Research Forum
DESCRIPTION:Forum Highlights \n\nOral and poster presentations on\n\nTheoretical foundations of data science\nData science methodology\nData science applications in any research domain\nSocial impact of data science research\n\n\n\n\nHow to engage industry workshop\n\nAdrian Fortino\, Partner (Mercury Fund)\nMike Psarouthakis\, Director (U-M Venture Center)\nKevyn Collins-Thompson\, Associate Professor\, U-M School of Information\nMike Cafarella\, Associate Professor\, U-M Computer Science Engineering\n\n\n\n\nKeynote by Chris Rozell\n\n\nNetworking Reception\n\nAll presentations will come from submissions in response to our call for abstracts\n Abstract Submission Deadline: October 23\, 2017\n We welcome submission from all U-M data science researchers (faculty\, staff\, trainees) \nPlease register for this event.  Please also see the call for abstracts for instruction\, and submit through the Abstract Submission Form. \nPreliminary Schedule
URL:https://micde.umich.edu/event/data-science-research-forum/
LOCATION:Michigan League\, 911 N. University\, Ann Arbor\, MI\, 48104\, United States
CATEGORIES:Conference,Data Science,Seminar
GEO:42.2796269;-83.7374973
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171129T153000
DTEND;TZID=America/Detroit:20171129T163000
DTSTAMP:20260606T231420
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
UID:10000116-1511969400-1511973000@micde.umich.edu
SUMMARY:MICDE 2017 Catalyst Grants Informational Session
DESCRIPTION:MICDE seeks proposals for innovative research projects in computational science that combine elements of mathematics\, computer science\, and cyberinfrastructure. Of interest is innovative computational research in any emerging area\, including but not limited to (a) applications such as neuroscience\, ecology\, environmental science\, evolutionary biology\, human-made complex systems\, urban infrastructure and energy\, (b) frameworks for scientific software\, exascale\, quantum\, or neuromorphic computing\, and (c) concepts such as computations to decisions. The aim of the Catalyst Grants program is to advance projects that have the potential to attract additional external funding. Priority will be given to high-impact projects with potential to eventually attract external funding. MICDE expects to fund 3-4 one-year projects at up to $100\,000 each. \nIn this informational session\, MICDE officials will clarify the program’s intent\, answer questions and facilitate team formation among attendees. \nPlease pre-register using this google form. You’ll need to be signed into your umich account. The session will be broadcasted via this bluejeans link. For more information go to https://live-umor-micde.pantheonsite.io/grants/catalyst-grants/
URL:https://micde.umich.edu/event/micde-2017-catalyst-grants-informational-session/
LOCATION:Space 2435 North Quad\, 105 S. State St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Info Session
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171124T180000
DTEND;TZID=America/Detroit:20171124T190000
DTSTAMP:20260606T231420
CREATED:20230905T171416Z
LAST-MODIFIED:20230905T171416Z
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SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-7/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171121T133000
DTEND;TZID=America/Detroit:20171121T143000
DTSTAMP:20260606T231420
CREATED:20230905T171414Z
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SUMMARY:MICDE Seminar: Edward Maginn\, Department of Chemical and Biomolecular Engineering\, University of Notre Dame
DESCRIPTION:Bio: Edward Maginn received his BS in chemical engineering from Iowa State University and his PhD in chemical engineering from the University of California\, Berkeley. Prior to attending graduate school\, he worked as a process engineer for Procter and Gamble. He has been on the Notre Dame faculty since 1995 and currently holds the Dorini Family Chair of Energy Studies in the Department of Chemical and Biomolecular Engineering. He is also the chair of the department\, and was formerly the Associate Dean for Academic Programs in the Graduate School. He has won a number of awards\, including the Early Career Award from the Computational Molecular Science and Engineering Forum of the American Institute of Chemical Engineers\, the ASEE Dow Outstanding New Faculty Award\, the BP College of Engineering Outstanding Teacher Award and the NSF Career award. He is a Fellow of the American Association for the Advancement of Science and is a trustee of the CACHE Corporation. His research focuses on the development and use of atomistic molecular dynamics and Monte Carlo simulation methods to study the thermodynamic and transport properties of materials\, with special emphasis on ionic systems important in energy storage and use. \nUsing Molecular Modeling to Design New Fluids for Energy Storage and Carbon Capture\nLiquids that contain charged species\, such as electrolytes and ionic liquids\, have many important technological applications in fields such as energy storage\, separations\, and catalysis. By changing the structure of the molecules or employing mixtures\, the properties of these fluids can be altered significantly. The key questions are: How should I change the structure of the molecule or ion to get the properties I want? What type of additives should I use to improve performance? To answer these and related questions\, we use atomistic-level simulations to compute structural\, thermodynamic and transport properties of these systems. We are able to provide molecular-level explanations for experimental observations\, and we can predict properties of systems that may not yet have even been made in the laboratory. \nIn the first part of this talk\, I will describe molecular modeling research directed at improving the performance of electrolytes used in next generation “beyond lithium” batteries. Electrolytes are a critical component of batteries\, since they transport ions from the cathode to the anode during charging\, then in the reverse direction in releasing energy on discharge. Electrolytes play a leading role in a battery’s capacity for energy storage\, its lifetime and the safety of the battery. The electrolyte in a conventional lithium-ion battery consists of a lithium salt dissolved in an organic solvent. The electrolytes for next generation “beyond lithium” batteries will require new salt-solvent combinations.  Our simulations probe the way in which different electrolyte formulations\, charge carriers and additives impact the structure and dynamics of these liquids. \nIn the second half of the talk\, I will show how these same kinds of simulations can be used to develop new ionic liquids that can be used for CO2 separations / capture. Ionic liquids are pure salts that are liquid at ambient temperatures. Because they have essentially no vapor pressure and readily dissolve CO2\, people have been interested in using them for carbon capture. I will describe how our simulations have been successful in identifying new ionic liquids with properties tuned for use as conventional liquid absorbents or as supported ionic liquid membranes. \nThis is a joint seminar with the department of Chemical Engineering. Prof. Maginn is being hosted by Prof. Mayes (Chemical Engineering). If you are interested in meeting him during his visit please send an email to mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-edward-maginn-department-of-chemical-and-biomolecular-engineering-university-of-notre-dame/
CATEGORIES:Featured Events,MICDE Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171117T180000
DTEND;TZID=America/Detroit:20171117T190000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000621-1510941600-1510945200@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-2/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171110T180000
DTEND;TZID=America/Detroit:20171110T190000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000620-1510336800-1510340400@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-3/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171110T150000
DTEND;TZID=America/Detroit:20171110T160000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
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SUMMARY:MICDE Seminar: Chris Rycroft\, Department of Applied Mathematics\, Harvard University
DESCRIPTION:Bio: Chris Rycroft is an Assistant Professor of Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University. From 2010–2013\, he was a Morrey Assistant Professor in the UC Berkeley Mathematics Department\, and he was involved in the Bay Area Physical Sciences-Oncology where he collaborated with several experimental groups at Berkeley and UC San Francisco\, on using computational modeling to understand the role of mechanical forces between cells and their environment. Prof. Rycroft’s research focuses on mathematical modeling and scientific computation\, particularly for interdisciplinary applications in science and engineering. He works on a variety of problems\, and has collaborated in a number of fields including physics\, biology\, materials science\, and mechanical engineering. His current interests include questions that relate to the mechanics of materials\, numerical algorithms\, and geometry. Several of his recent projects relate to energy production and efficiency\, such as modeling bulk metallic glasses\, and developing high-throughput screening techniques to find advanced materials for carbon capture applications. He has also released several software libraries\, including Voro++ for three-dimensional computations of the Voronoi tessellation. \nThe reference map technique for simulating complex materials and multi-body interactions\nConventional computational methods often create a dilemma for fluid-structure interaction problems. Typically\, solids are simulated using a Lagrangian approach with grid that moves with the material\, whereas fluids are simulated using an Eulerian approach with a fixed spatial grid\, requiring some type of interfacial coupling between the two different perspectives. Here\, a fully Eulerian method for simulating structures immersed in a fluid will be presented. By introducing a reference map variable to model finite-deformation constitutive relations in the structures on the same grid as the fluid\, the interfacial coupling problem is highly simplified. The method is particularly well suited for simulating soft\, highly-deformable materials and many-body contact problems\, and several examples from engineering and biology will be presented. This is joint work with Ken Kamrin (MIT). \nThis is a joint seminar with the Interdisciplinary Applied Mathematics seminar series. \nProf. Rycroft is being hosted by Prof. Alben (Mathematics). If you would like to meet him please email Prof. Alben at alben@umich.edu or Dr. Mariana Carrasco-Teja at mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-chris-rycroft-department-of-applied-mathematics-harvard-university/
LOCATION:1084 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171108T140000
DTEND;TZID=America/Detroit:20171108T150000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
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SUMMARY:MICDE Seminar: Giulia Galli\, Department of Molecular Engineering\, University of Chicago
DESCRIPTION:Bio: Giulia Galli is the Liew Family Professor of Electronic Structure and Simulations in the Institute for Molecular Engineering at the University of Chicago. She also holds a Senior Scientist position at Argonne National Laboratory (ANL) and she is a Senior Fellow of the UChicago/ANL Computational Institute. Prior to joining U Chicago and ANL\, she was Professor of Chemistry and Physics at UC Davis (2005-2013) and the head of the Quantum Simulations group at the Lawrence Livermore National Laboratory (1998-2005).\nShe holds a Ph.D. in Physics from the International School of Advanced Studies (SISSA) in Trieste\, Italy. She is a Fellow of the American Physical Society (APS) and of the AAAS. She is the recipient of an award of excellence from the Department of Energy (2000) and of the Science and Technology Award from the Lawrence Livermore National Laboratory (2004). She is currently the director of MICCoM (Midwest Integrated Center for Computational Materials)\, established by DOE in 2015. Her research activity is focused on the development and use of theoretical and computational tools to understand and predict the properties and behavior of materials (solids\, liquids and nanostructures) from first principles. \nMaterials discovery and scientific design by computation: what does it take?\nSubstantial progress has been made in the last three decades in understanding and predicting the fundamental properties of materials and molecular systems from first principles\, employing electronic structure methods and atomistic simulations. Using specific examples\, I will discuss some predictions obtained for materials for energy conversion processes (photo-catalysis of water and solar cells) as well as some of the major challenges involved in enabling scientific discoveries by computation; in particular I will touch upon theoretical validation; and collection and verification of data generated by simulations. I will also discuss some of the theoretical and algorithmic advances required to broaden the scope of properties accessible by current ab initio simulations. \nProfessor Galli is being hosted by Prof. Siegel (Mechanical Engineering). If you would like to meet her during her visit please email mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-giulia-galli-department-of-molecular-engineering-university-of-chicago/
CATEGORIES:Featured Events,MICDE Seminar Series
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2017/09/Giulia-Galli.png
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171103T180000
DTEND;TZID=America/Detroit:20171103T190000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000619-1509732000-1509735600@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-4/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171102T140000
DTEND;TZID=America/Detroit:20171102T150000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
LAST-MODIFIED:20260522T153005Z
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SUMMARY:MICDE Seminar: Thomas Devereaux\, Photon Science\, Stanford University
DESCRIPTION:Bio: Professor Devereaux received his Ph.D. in Physics from the University of Oregon in 1991\, M.S. from University of Oregon in 1988\, and B.S from New York University in 1986. Professor Devereaux is currently the Director of the Stanford Institute for Materials and Energy Sciences (SIMES)\, the Associate Lab Director (ALD) for Photon Science\, a professor in the Photon Science Faculty at SLAC National Accelerator Laboratory and Stanford University and a Senior Fellow of the Precourt Institute for Energy. SIMES is a joint institute between Stanford main campus and SLAC\, a national laboratory\, focusing on scientific foundations related to the energy challenge facing our society. Professor Devereaux was a Post-doctoral Fellow at the Max Planck Institut\, Stuttgart\, (1991-1993)\, a Post-doctoral Fellow at the University of California\, Davis\, CA\, (1993-1996)\, an Assistant Professor at The George Washington University\, Washington\, DC\, (1996-1999)\, and an Associate Professor (1999-2006) and Professor (2006-2007) at the University of Waterloo\, Waterloo\, ON\, Canada.\nHis main research interests lie in the areas of theoretical condensed matter physics and computational physics. His research effort focuses on using the tools of computational physics to understand quantum materials. Fortunately\, we are poised in an excellent position as the speed and cost of computers have allowed us to tackle heretofore unaddressed problems involving interacting systems. The goal of his research is to understand electron dynamics via a combination of analytical theory and numerical simulations to provide insight into materials of relevance to energy science. His group carries out numerical simulations on SIMES’ high-performance supercomputer\, the National Energy Research Scientific Computing Center (NERSC)\, and other US and Canadian computational facilities. The specific focus of the group is the development of numerical methods and theories of photon-based spectroscopies of strongly correlated materials.\nProfessor Devereaux’s awards include: U. S. Department of Education Fellowship (1989-1991); Junior Scholar Incentive Award\, George Washington University (1998); Research Fellowship of the Alexander von Humboldt Foundation (2002-2006); Premier’s Research Excellence Award\, Province of Ontario (2003); Scientist Research Fellowship\, Embassy of France (2005); and Fellow of the American Physics Society (2008). \nLight controlled topological phase transitions in multi-orbital and frustrated magnetic systems\nSpurred by recent progress in melting\, enhancement and induction of electronic order out of equilibrium\, a tantalizing prospect concerns instead accessing transient Floquet steady states via broad pump pulses\, to affect electronic properties. Here\, we consider a two-pronged approach to manipulate the topology of a band insulator\, as well as topological order in a Mott insulator. We first consider monolayer transition-metal dichalcogenides (TMDCs) [1]\, and show that their low-energy description as massive 2D relativistic fermions fails to hold for optical pumping. Instead\, the added complexity of a realistic materials description leads to a novel mechanism to optically induce topologically-protected chiral edge modes\, facilitating optically-switchable conduction channels that are insensitive to disorder. We develop a strategy to understand non-equilibrium Floquet-Bloch bands and topological transitions directly from ab initio calculations\, and illustrate for the example of WS2 that control of chiral edge modes can be dictated solely from symmetry principles and is not qualitatively sensitive to microscopic materials details. Second\, we extend these ideas to strongly correlated systems and show that pumping frustrated Mott insulators with circularly-polarized light can drive the effective spin system across a phase transition to a chiral spin liquid (CSL) [2]. We show that the transient time evolution of a Kagome lattice Hubbard model is well captured by an effective spin description\, where circular polarization promotes a staggered scalar spin chirality Si . (Sj x Sk) directly to the Hamiltonian level. We fingerprint the ensuing phase diagram and find a stable photo-induced CSL in proximity to the equilibrium ground state. The results presented suggest new avenues to marry dynamical symmetry breaking\, strong interactions\, and ab initio materials modelling\, to access elusive phase transitions that are not readily accessible in equilibrium. \nReferences:\n[1] M. Claassen et al\, Nature Comm. 7\, 13074 (2016).\n[2] M. Claassen et al\, arXiv:1611.07964\, to appear in Nature Communications. \nThis is a joint CM Theory seminar. Prof. Devereaux is being hosted by Prof. Gull (Physics). If you are interested in meeting with him during his visit please send an email to mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-thomas-devereaux-photon-science-stanford-university/
LOCATION:4448 East Hall\, 530 Church St\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171027T180000
DTEND;TZID=America/Detroit:20171027T190000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000618-1509127200-1509130800@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-5/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
GEO:42.292322;-83.713272
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171025T150000
DTEND;TZID=America/Detroit:20171025T160000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
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SUMMARY:MICDE Seminar: Irina Tezaur\, Extreme Scales Data Science and Analytics Department\, Sandia National Laboratories
DESCRIPTION:Bio: Dr. Irina Tezaur (f.k.a. Dr. Irina Kalashnikova) is a Principal Member of Technical Staff (PMTS) in the Extreme Scales Data Science & Analytics Department (Org. 8759) at Sandia National Laboratories in Livermore\, CA. Prior to joining this group\, from October 2011 to September 2014\, she was SMTS in the Computational Mathematics Department (Org. 1442) at Sandia in Albuquerque\, NM. She received her Ph.D. in Computational and Mathematical Engineering (CME) from Stanford University in 2011. Her advisor at Stanford was Professor Charbel Farhat and I was a member of the Farhat Research Group (FRG). Her Bachelors and Masters degrees are in pure mathematics\, awarded by the University of Pennsylvania in 2006. Dr. Tezaur’s research interests are numerical solution to PDEs\, mixed/hybrid finite element methods\, stability and convergence properties of numerical methods\, Reduced Order Modeling (ROM) and simulation-based analysis of fluid-structure interaction that she currently applies to climate modeling. \nNext-generation modeling & simulation of large-scale ice sheets towards probabilistic sea-level change projections\nRecent observations show that both the Greenland and Antarctic ice sheets are losing mass at increasingly rapid rates [1]. In its fourth assessment report (AR4)\, the Intergovernmental Panel on Climate Change (IPCC) declined to include estimates of future sea-level change from dynamics of the polar ice sheets due to the inability of ice sheet models to mimic or explain observed dynamic behaviors\, such as the acceleration and thinning then occurring on several of Greenland’s large outlet glaciers [2]. In recent years\, there has been a push to develop “next generation” land-ice models and codes for integration into global Earth System Models (ESMs). Unlike their predecessors\, these codes: (1) are able to perform realistic\, high-resolution\, continental scale simulations\, (2) are robust\, efficient and scalable on next-generation hybrid systems (multi-core\, many-core\, GPU\, Intel Xeon Phi)\, and (3) possess built-in advanced analysis capabilities (e.g.\, sensitivity analysis\, optimization\, uncertainty quantification). This talk will give an overview of the Albany/FELIX (Finite Elements for Land Ice eXperiments) [3] next-generation land-ice dynamical core (dycore) that is under development at Sandia National Laboratories as a part of a Department of Energy (DOE) SciDAC-funded project aimed at providing probabilistic sea-level projections from extreme-scale ice sheet and earth system models. This dycore is currently being integrated in to the DOE’s Acelerated Climate Model for Energy (ACME)\, which will be used to calculate anticipated 21st sea-level change projections\, including uncertainty bounds. It is widely accepted that land-ice behaves like a very viscous\, shear-thinning\, non-Newtonian fluid\, similar to lava flow. Typically\, ice sheets are modeled using a quasi-static model in which a steady momentum-balance system for the ice velocities is coupled to dynamic equations for the ice thickness and temperature. The Albany/FELIX dycore is based on the so-called “First-Order Stokes” equations for the ice momentum balance [4]\, an attractive alternative to the more expensive “Full Stokes” model because of its reduced computational cost. Following an overview of our land-ice model and project\, I will describe some of the algorithms and software we have developed as a part of this project that have contributed to our dycore’s robustness and scalability. These include: robust automatic-differentiation-based nonlinear solvers\, scalable algebraic-multigrid-based iterative linear solvers [5]\, adaptive mesh refinement capabilities\, and stable semi-implicit First-Order Stokes-thickness coupling methods. I will also discuss some of the advanced analysis capabilities in Albany/FELIX\, namely a large-scale inversion approach we have developed for obtaining optimal ice initial conditions [6]\, our workflow towards quantifying uncertainties in land-ice models\, and performance-portability of the Albany/FELIX code to new and emerging architectures using the Kokkos library [7]. I will show results which demonstrate that the Albany/FELIX dycore is scalable\, fast and robust for production-scale land-ice problems on state-of-the-art HPC machines. I will also discuss results from a recent validation study in which Albany/FELIX was used to simulate the Greenland ice sheet during the period 1991-2013 with realistic climate forcing\, and the simulation data were compared with observational data collected by NASA satellites [8]. \nThis work was done in collaboration with Irina Demeshko\, Mike Eldred\, Matt Hoffman\, John Jakeman\, Mauro Perego\, Steve Price\, Andy Salinger\, Ray Tuminaro and Jerry Watkins. \nDr. Tezaur is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet her please email mcteja@umich.edu \n[1] I. Velicogna. Increasing rates of ice mass loss from the Greenland and Antarctic ice sheets revealed by GRACE. Geophysical Research Letters\, 36 (19) L19503\, 2009.\n[2] S. Solomon\, D. Qin\, M. Manning\, Z. Chen\, M. Marquis\, K. Averyt\, M. Tignor\, H. Miller. Climate change 2007: The physical science basis\, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change\, Cambridge Univ. Press\, Cambridge\, UK\, 2007.\n[3] I. Tezaur\, M. Perego\, A. Salinger\, R. Tuminaro\, S. Price. Albany/FELIX: A Parallel\, Scalable and Robust Finite Element Higher-Order Stokes Ice Sheet Solver Built for Advanced Analysis\, Geosci. Model Develop. 8 (2015) 1-24.\n[4] J.K. Dukowicz\, S.F. Price\, W. Lipscomb. Consistent approximations and boundary conditions for ice-sheet dynamics from a principle of least action. J. Glaciol.\, 56 (197) (2010) 480-496.\n[5] R. Tuminaro\, M. Perego\, I. Tezaur\, A. Salinger\, S. Price. A matrix dependent/algebraic multigrid approach for extruded meshes with applications to ice sheet modeling\, SIAM J. Sci. Comput. 38 (5) (2016) C504-C532.\n[6] M. Perego\, S. Price\, G. Stadler. Optimal initial conditions for coupling ice sheet models to earth system models\, J. Geophys. Res.\, 119 (2014) 1894-1917.\n[7] H.C. Edwards\, C.R. Trott\, D. Sunderland. Kokkos: Enabling manycore performance portability through polymorphic memory access patterns. J. Par. and Distr. Comput.\, 74 (12) 3202–3216\, 2014.\n[8] S. Price\, M. Hoffman\, J. Bonin\, T. Neumann\, I. Howat\, J. Guerber\, I. Tezaur\, J. Saba\, J. Lanaerts\, D. Chambers\, W. Lipscomb\, M. Perego\, A. Salinger\, R. Tuminaro. An ice sheet model validation framework for the Greenland ice sheet\, Geosci. Model Dev. 10 (2017) 255-270
URL:https://micde.umich.edu/event/micde-seminar-irina-tezaur-extreme-scales-data-science-analytics-department-sandia-national-laboratories/
LOCATION:1006 H.H. Dow\, 2300 Hayward St\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171020T180000
DTEND;TZID=America/Detroit:20171020T190000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000617-1508522400-1508526000@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-6/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
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GEO:42.292322;-83.713272
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171019T153000
DTEND;TZID=America/Detroit:20171019T163000
DTSTAMP:20260606T231420
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
UID:10000090-1508427000-1508430600@micde.umich.edu
SUMMARY:MICDE Seminar: Panos Papadopoulos\, Department of Mechanical Engineering\, University of California\, Berkeley
DESCRIPTION:Bio: Panos Papadopoulos is a Professor of Mechanical Engineering at the University of California\, Berkeley\, and director of the Computational Solid Mechanics Laboratory. After obtaining his Diploma in Civil Engineering from the Aristotle University\, Greece\, he moved to California to pursue his graduate studies. He obtained his M. Sc. and Ph.D. in Civil Engineering from UC Berkeley. His research involves experimental\, analytical and computational studies of several mechanics systems. Prof. Papadopoulus develops and applied the finite element method to problems in biomechanics\, dynamics of pseudo-rigid bodies\, mechanics of continues media\, plasticity\, materials science and contact mechanics. \nMultiscale Modeling in Continuum Mechanics: A connection to the Irving-Kirkwood procedure\nThis talk describes a method for extending the classical Irving-Kirkwood procedure used in statistical mechanics for extracting local fluxes to the problem of continuum-on-continuum multiscale modeling. Expressions for stress and heat flux derived here are contrasted to those obtained using the standard Hill-Mandel approach. The polar nature of the macroscopic solid and the role of multiscale invariance are also addressed in the context of this method. Applications are explored within the finite element-based homogenization of solids. \nProf. Papadopoulos is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet with him please send an email to mcteja@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-panos-papadopoulos-department-of-mechanical-engineering-university-of-california-berkeley/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171019T120000
DTEND;TZID=America/Detroit:20171019T133000
DTSTAMP:20260606T231420
CREATED:20230905T171405Z
LAST-MODIFIED:20230905T171405Z
UID:10000115-1508414400-1508419800@micde.umich.edu
SUMMARY:Mechanical Engineering Seminar: Mark Owkes\, Department of Mechanical and Industrial Engineering\, Montana State University
DESCRIPTION:Bio: Mark Owkes is an Assistant Professor in the department of Mechanical and Industrial Engineering at Montana State University. He earned a BS in Mechanical Engineering from Clarkson University in 2008. He subsequently attained an MS in Mechanical Engineering from the University of Colorado at Boulder in 2011 under the direction of Prof. Olivier Desjardins. He continued his work under Prof. Desjardins at Cornell University where he earned his Ph.D. in May 2014. Mark’s research interests include the development of numerical methods for capturing gas-liquid interfaces in multiphase flow simulations. His simulations of primary atomization provide insight into the physical phenomena important in the break-up of a liquid jet into droplets. Notably\, he has developed both a level set and a volume-of-fluid interface capturing schemes and multiple approaches to compute the curvature of a gas-liquid interface which is important for accurate surface tension forces. \nGas-Liquid Flows: Numerical Methods through Simulations on Supercomputers\nGas-liquid flows exist within many engineering devices including fuel injectors\, wave energy extraction devices\, fire suppression systems\, and PEM fuel cells. Many of these flows are challenging to explore experimentally and computational fluid dynamics (CFD) simulations offer an alternative and useful approach to advance our understanding. For example\, the breakup of liquid fuel into droplets via atomization has a direct effect on combustion efficiency and pollutant formation\, yet a fundamental understanding of the complex process is absent. Laboratory experiments are inherently difficult to conduct because atomizing jets produce a large number of opaque droplets that hinder optical access to the breakup dynamics. With increasing computational resources and advancements in numerical methods\, computational fluid dynamics (CFD) has emerged as a promising tool to investigate the fundamental nature of atomization. In this presentation\, I will present an overview of difficulties arising due to the discontinuities that exist at the gas-liquid interface and recent advances in numerical methods that overcome these challenges. Then I will discuss efforts to improve the the usefulness of the very large data-sets that result from CFD simulations. Details on computing the curvature of a gas-liquid interface\, implementing contact line dynamics\, performing physics extraction\, and coupling gas-liquid flow calculations with uncertainty quantification we be discussed.
URL:https://micde.umich.edu/event/mechanical-engineering-seminar-mark-owkes-department-of-mechanical-and-industrial-engineering-montana-state-university/
LOCATION:1012 EECS\, 1301 Beal Ave\, Ann Arbor\, MI\, 48104\, United States
CATEGORIES:Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171016T090000
DTEND;TZID=America/Detroit:20171016T130000
DTSTAMP:20260606T231420
CREATED:20230905T171438Z
LAST-MODIFIED:20230905T171438Z
UID:10000094-1508144400-1508158800@micde.umich.edu
SUMMARY:[MICDE] 1st Workshop on Computational Neuroscience
DESCRIPTION:MICDE and the Graduate Program in Neuroscience have organized the first Computational Neuroscience Workshop. The goal of the event is to bring together the U-M community of neuroscientists who use computational methods in their research\, and to start building new bridges across disciplines and departments. For more information and to register…
URL:https://micde.umich.edu/event/micde-1st-workshop-on-computational-neuroscience/
LOCATION:Space 2435 North Quad\, 105 S. State St.\, Ann Arbor\, MI\, 48109\, United States
GEO:42.2807324;-83.7400253
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171013T180000
DTEND;TZID=America/Detroit:20171013T190000
DTSTAMP:20260606T231420
CREATED:20230905T171438Z
LAST-MODIFIED:20230905T171438Z
UID:10000616-1507917600-1507921200@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2-8/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171006T180000
DTEND;TZID=America/Detroit:20171006T190000
DTSTAMP:20260606T231420
CREATED:20230905T171439Z
LAST-MODIFIED:20230905T171439Z
UID:10000615-1507312800-1507316400@micde.umich.edu
SUMMARY:SC2 Machine Learning Collaborative Workshop
DESCRIPTION:Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. The Scientific Computing Student Club (SC2) has organized this workshop for students\, and all interested individuals\, with the goal of learning existing ML tools that can be easily integrate in research workflow.  Weekly meetings on Fridays @ 6:00 pm\, except November 24\, 2017. More information…
URL:https://micde.umich.edu/event/sc2-machine-learning-collaborative-workshop-2/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events
ATTACH;FMTTYPE=image/png:https://micde.umich.edu/wp-content/uploads/2022/04/SC2_simple.png
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171003T160000
DTEND;TZID=America/Detroit:20171003T170000
DTSTAMP:20260606T231420
CREATED:20230905T171439Z
LAST-MODIFIED:20230905T171439Z
UID:10000089-1507046400-1507050000@micde.umich.edu
SUMMARY:MICDE Seminar: Margaret Cheung\, Department of Physics\, University of Houston
DESCRIPTION:Bio: Margaret Cheung is an Associate Professor of Physics at the University of Houston. She graduated from the National Taiwan University with a bachelor’s degree in chemistry and received her Ph.D. in physics from the University of California\, San Diego. She carried out theoretical biological physics and bioinformatics research as a Sloan Postdoctoral Fellow at the University of Maryland and started her lab at the University of Houston in 2006. Professor Cheung’s research is in the field of protein folding inside a cell\, calmodulin dependent calcium signaling\, and quantum efficiency in artificial photosynthetic materials. She is particularly interested in developing coarse-grained models for protein dynamics in crowded systems\, creating multi-physics models that bridge dynamics across wide temporal and spatial scales\, and designing computational algorithms that effectively integrate novel high-performance resources. These systems can then be applied for understanding of biological function and for developing therapeutic strategies. She is a fellow of the American Physical Society and a Senior Scientist at the Center for Theoretical Biological Physics at Rice University. \nMolecular Underpinning of Postsynaptic Calmodulin-dependent Calcium Signaling\nCalcium (Ca2+) is exquisitely utilized by a cell for transducing external stimuli through its gradient of extracellular (~1000 μM) and intracellular (~0.1 μM) concentration. A broad spectrum of Ca2+ signals are encoded by protein calmodulin (CaM) through specific binding with various targets regulating CaM-dependent Ca2+ signaling pathways in neurons. I will focus on binding between CaM and two specific targets\, Ca2+/CaM-dependent protein kinase II (CaMKII) and neurogranin (Ng)\, as they antagonistically regulate CaM-dependent Ca2+ signaling pathways in neurons. I will show the impact of bound calmodulin (CaM)-target compound structure on the affinity of calcium (Ca2+) by integrating coarse-grained models and all-atomistic simulations with non-equilibrium physics. We discovered the molecular underpinnings of lowered affinity of Ca2+ for CaM in the presence of Ng by showing that the N-terminal acidic region of Ng peptide pries open the β-sheet structure between the Ca2+ binding loops particularly at C-domain of CaM\, enabling Ca2+ release. In contrast\, CaMKII peptide increases Ca2+ affinity for the C-domain of CaM by stabilizing the two Ca2+ binding loops. Through distinctive structural differences in the bound complexes of apoCaM-Ng13-49 and holoCaM-CaMKII\, CaM’s affinity for Ca2+ is delineated by its progressive mechanism of target binding. I will discuss them in the context of evolution and in the crowded environment. \nProf. Cheung is being hosted by Prof. Geva (Chemistry)
URL:https://micde.umich.edu/event/micde-seminar-margaret-cheung-department-of-physics-university-of-houston/
LOCATION:CHEM 1640\, 930 N University\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170925T170000
DTEND;TZID=UTC:20170925T180000
DTSTAMP:20260606T231420
CREATED:20230905T171439Z
LAST-MODIFIED:20230905T171439Z
UID:10000055-1506358800-1506362400@micde.umich.edu
SUMMARY:Graduate Studies in Computational & Data Sciences Info Session - Central Campus
DESCRIPTION:Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided \n\nThe Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their studies. It is a joint degree program\, with students earning a Ph.D. from their current departments\, “… and Scientific Computing” — for example\, “Ph.D. in Aerospace Engineering and Scientific Computing.”\nThe Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.\nThe Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:\n1) Modeling — Understanding of core data science principles\, assumptions and applications;\n2) Technology — Knowledge of basic protocols for data management\, processing\, computation\, information extraction\, and visualization;\n3) Practice — Hands-on experience with real data\, modeling tools\, and technology resources.
URL:https://micde.umich.edu/event/graduate-studies-in-computational-data-sciences-info-session-central-campus-f2017/
LOCATION:2001 LSA Building\, 500 State St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Info Session
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X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=2001 LSA Building 500 State St. Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=500 State St.:geo:-83.7413068,42.2761921
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20170922T170000
DTEND;TZID=America/Detroit:20170922T180000
DTSTAMP:20260606T231420
CREATED:20230905T171439Z
LAST-MODIFIED:20230905T171439Z
UID:10000103-1506099600-1506103200@micde.umich.edu
SUMMARY:MIDAS Seminar: Jimmy Soni & Rob Goodman\, authors of "A Mind at Play: How Claude Shannon Invented the Information Age"
DESCRIPTION:Authors Jimmy Soni and Rob Goodman will talk about their new book on U-M alum Claude Shannon: “A Mind at Play: How Claude Shannon Invented the Information Age.” \nThe talk will be followed by a book signing. \nJimmy Soni is an author and editor. He has worked as an editor at The New York Observer and The Washington Examiner. He has worked as a speechwriter\, and his writing and commentary have appeared in Slate\, The Atlantic\, and CNN. He is a graduate of Duke University and was named to the Forbes 30 Under 30. With Rob Goodman\, he is the coauthor of Rome’s Last Citizen: The Life and Legacy of Cato\, Mortal Enemy of Caesar and A Mind at Play: How Claude Shannon Invented the Information Age. \nRob Goodman is a doctoral candidate at Columbia University and a former congressional speechwriter. He has written for Slate\, The Atlantic\, Politico Magazine\, and The Chronicle of Higher Education. His scholarly work has appeared in History of Political Thought\, the Kennedy Institute of Ethics Journal\, and The Journal of Medicine and Philosophy. With Jimmy Soni\, he is the coauthor of Rome’s Last Citizen: The Life and Legacy of Cato\, Mortal Enemy of Caesar and A Mind at Play: How Claude Shannon Invented the Information Age.
URL:https://micde.umich.edu/event/midas-seminar-jimmy-soni-rob-goodman-authors-of-a-mind-at-play-how-claude-shannon-invented-the-information-age/
LOCATION:Rackham Building\, 4th Floor\, 915 E. Washington\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:MIDAS Seminar Series
GEO:42.2807892;-83.7381556
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Rackham Building 4th Floor 915 E. Washington Ann Arbor MI 48109 United States;X-APPLE-RADIUS=500;X-TITLE=915 E. Washington:geo:-83.7381556,42.2807892
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20170921T170000
DTEND;TZID=UTC:20170921T180000
DTSTAMP:20260606T231420
CREATED:20230905T171439Z
LAST-MODIFIED:20230905T171439Z
UID:10000056-1506013200-1506016800@micde.umich.edu
SUMMARY:Graduate Studies in Computational & Data Sciences Info Session - North Campus
DESCRIPTION:Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided \n\nThe Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation\, computational methods\, or algorithms for advanced computer architectures in their studies. It is a joint degree program\, with students earning a Ph.D. from their current departments\, “… and Scientific Computing” — for example\, “Ph.D. in Aerospace Engineering and Scientific Computing.”\nThe Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.\nThe Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:\n1) Modeling — Understanding of core data science principles\, assumptions and applications;\n2) Technology — Knowledge of basic protocols for data management\, processing\, computation\, information extraction\, and visualization;\n3) Practice — Hands-on experience with real data\, modeling tools\, and technology resources.
URL:https://micde.umich.edu/event/graduate-studies-in-computational-data-sciences-info-session-north-campus-2017f/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Info Session
GEO:42.2914823;-83.7138452
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=Johnson Rooms Lurie Engineering Center 3rd Floor 1221 Beal Ave. Ann Arbor MI United States;X-APPLE-RADIUS=500;X-TITLE=1221 Beal Ave.:geo:-83.7138452,42.2914823
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20170713T090000
DTEND;TZID=America/Detroit:20170713T173000
DTSTAMP:20260606T231420
CREATED:20230905T171439Z
LAST-MODIFIED:20230905T171439Z
UID:10000084-1499936400-1499967000@micde.umich.edu
SUMMARY:Symposium: Advances on Turbulence Modeling
DESCRIPTION:The Center for Data-Driven Computational Physics and NASA are sponsoring the event to discuss the state­ of­ the ­art in turbulence modeling from an academic and an industrial perspective\, and place some of the newer developments in RANS modeling (such as uncertainty quantification\, data­-driven modeling\, etc.) in the context of main­stream turbulence modeling. \nSpeakers include: \n\nFlorian Menter\, Ansys\nSuad Jakirlic\, TU Darmstadt\nRobert Moser\, U. Texas\n\nFor more details and to register go to http://turbgate.engin.umich.edu/symposium/
URL:https://micde.umich.edu/event/symposium-advances-on-turbulence-modeling/2017-07-13/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20170712T090000
DTEND;TZID=America/Detroit:20170712T173000
DTSTAMP:20260606T231420
CREATED:20230905T171424Z
LAST-MODIFIED:20230905T171424Z
UID:10000083-1499850000-1499880600@micde.umich.edu
SUMMARY:Symposium: Advances on Turbulence Modeling
DESCRIPTION:The Center for Data-Driven Computational Physics and NASA are sponsoring the event to discuss the state­ of­ the ­art in turbulence modeling from an academic and an industrial perspective\, and place some of the newer developments in RANS modeling (such as uncertainty quantification\, data­-driven modeling\, etc.) in the context of main­stream turbulence modeling. \nSpeakers include: \n\nFlorian Menter\, Ansys\nSuad Jakirlic\, TU Darmstadt\nRobert Moser\, U. Texas\n\nFor more details and to register go to http://turbgate.engin.umich.edu/symposium/
URL:https://micde.umich.edu/event/symposium-advances-on-turbulence-modeling-2-2/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20170711T090000
DTEND;TZID=America/Detroit:20170711T173000
DTSTAMP:20260606T231420
CREATED:20230905T171437Z
LAST-MODIFIED:20230905T171437Z
UID:10000082-1499763600-1499794200@micde.umich.edu
SUMMARY:Symposium: Advances on Turbulence Modeling
DESCRIPTION:The Center for Data-Driven Computational Physics and NASA are sponsoring the event to discuss the state­ of­ the ­art in turbulence modeling from an academic and an industrial perspective\, and place some of the newer developments in RANS modeling (such as uncertainty quantification\, data­-driven modeling\, etc.) in the context of main­stream turbulence modeling. \nSpeakers include: \n\nFlorian Menter\, Ansys\nSuad Jakirlic\, TU Darmstadt\nRobert Moser\, U. Texas\n\nFor more details and to register go to http://turbgate.engin.umich.edu/symposium/
URL:https://micde.umich.edu/event/symposium-advances-on-turbulence-modeling-2/
END:VEVENT
END:VCALENDAR