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DTSTART;TZID=America/Detroit:20171110T150000
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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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DTSTART;TZID=America/Detroit:20171108T140000
DTEND;TZID=America/Detroit:20171108T150000
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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
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DTSTART;TZID=America/Detroit:20171103T180000
DTEND;TZID=America/Detroit:20171103T190000
DTSTAMP:20260604T111332
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
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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-2-4/
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:20171102T140000
DTEND;TZID=America/Detroit:20171102T150000
DTSTAMP:20260604T111332
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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:20260604T111332
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171025T150000
DTEND;TZID=America/Detroit:20171025T160000
DTSTAMP:20260604T111332
CREATED: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:20260604T111332
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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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20171019T153000
DTEND;TZID=America/Detroit:20171019T163000
DTSTAMP:20260604T111332
CREATED:20230905T171415Z
LAST-MODIFIED:20230905T171415Z
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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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DTSTART;TZID=America/Detroit:20171013T180000
DTEND;TZID=America/Detroit:20171013T190000
DTSTAMP:20260604T111332
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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-2-8/
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:20171006T180000
DTEND;TZID=America/Detroit:20171006T190000
DTSTAMP:20260604T111332
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
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DTSTART;TZID=America/Detroit:20171003T160000
DTEND;TZID=America/Detroit:20171003T170000
DTSTAMP:20260604T111332
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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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DTSTART;TZID=America/Detroit:20170317T110000
DTEND;TZID=America/Detroit:20170317T120000
DTSTAMP:20260604T111332
CREATED:20230905T171438Z
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SUMMARY:MICDE Seminar: Yongjie Jessica Zhang\, Mechanical Engineering and Biomedical Engineering\, Carnegie Mellon University
DESCRIPTION:Bio: Yongjie Jessica Zhang is a Professor in Mechanical Engineering at Carnegie Mellon University with a courtesy appointment in Biomedical Engineering. She received her B.Eng. in Automotive Engineering\, and M.Eng. in Engineering Mechanics from Tsinghua University\, China; and M.Eng. in Aerospace Engineering and Engineering Mechanics and Ph.D. in Computational Engineering and Sciences from Institute for Computational Engineering and Sciences (ICES)\, The University of Texas at Austin. After staying two years at ICES as a postdoctoral fellow\, she joined CMU in 2007 as an assistant professor\, and then was promoted to an associate professor in 2012 and a full professor in 2016. Her research interests include computational geometry\, mesh generation\, computer graphics\, visualization\, finite element method\, isogeometric analysis and their application in computational biomedicine\, material sciences and engineering. She has co-authored over 140 publications in peer-reviewed journals and conference proceedings\, and received the Autodesk Best Paper Award 1st Place in SIAM Conference on Solid and Physical Modeling 2015\, the Best Paper Award in CompIMAGE’16 conference and one of the 5 Most Highly Cited Papers Published in Computer-Aided Design during 2014-2016. She recently published a book entitled “Geometric Modeling and Mesh Generation from Scanned Images” with CRC Press\, Taylor & Francis Group. She is the recipient of Presidential Early Career Award for Scientists and Engineers\, NSF CAREER Award\, Office of Naval Research Young Investigator Award\, USACM Gallagher Young Investigator Award\, Clarence H. Adamson Career Faculty Fellow in Mechanical Engineering\, George Tallman Ladd Research Award\, and Donald L. & Rhonda Struminger Faculty Fellow. \nImage-Based Mesh Generation and Volumetric T-Spline Modeling for Isogeometric Analysis with Engineering Applications\nWith finite element method and scanning technology seeing increased use in many research areas\, there is an emerging need for high-fidelity geometric modeling and mesh generation of spatially realistic domains. This talk will highlight research in three areas: image-based mesh generation for complicated domains\, trivariate spline modeling for isogeometric analysis\, as well as biomedical\, material sciences and engineering applications. First Prof. Zhang will present advances and challenges in image-based geometric modeling and meshing along with a comprehensive computational framework\, which integrates image processing\, geometric modeling\, mesh generation and quality improvement with multi-scale analysis at molecular\, cellular\, tissue and organ scales. Different from other existing methods\, the presented framework supports five unique features: high-fidelity meshing for heterogeneous domains with topology ambiguity resolved; multiscale geometric modeling for biomolecular complexes; automatic all-hexahedral mesh generation with sharp feature preservation; robust quality improvement for non-manifold meshes; and guaranteed-quality meshing. These unique capabilities enable accurate\, stable\, and efficient mechanics calculation for many biomedicine\, materials science and engineering applications. As a new advancement of traditional finite element method\, isogeometric analysis (IGA) was proposed to integrate design and analysis. In the second part of this talk\, she will present her latest research on volumetric T-spline parameterization for IGA applications. For arbitrary-topology objects\, we first build a polycube whose topology is equivalent to the input geometry and it serves as the parametric domain for the following trivariate T-spline construction. Boolean operations\, geometry skeleton and centroidal Voronoi tessellation based surface segmentation are used to preserve surface features. A parametric mapping is then used to build a one-to-one correspondence between the input geometry and the polycube boundary. After that\, we choose the deformed octree subdivision of the polycube as the initial T-mesh\, and make it valid through pillowing\, quality improvement\, and applying templates or truncated subdivision schemes to handle extraordinary nodes. Weighted and truncated T-spline basis functions are derived to enable analysis-suitability\, including partition of unity and linear independence. The developed pipelines have been incorporated into commercial software such as Rhino and Abaqus. \nProf. Zhang is being hosted by Prof. Garikipati (Mechanical Engineering)
URL:https://micde.umich.edu/event/micde-seminar-yongjie-jessica-zhang-mechanical-engineering-and-biomedical-engineering-carnegie-mellon-university/
LOCATION:1200 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20170308T140000
DTEND;TZID=America/Detroit:20170308T150000
DTSTAMP:20260604T111332
CREATED:20230905T171438Z
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SUMMARY:SC2/MICDE Seminar: Eric Jankowski\, Materials Science and Engineering\, Boise State University
DESCRIPTION:Bio: Eric Jankowski is an assistant professor of Materials Science and Engineering at Boise State University. He earned his PhD in Chemical Engineering from the University of Michigan in 2012\, where he developed computational tools to study the self-assembly of nanoparticles. These tools leveraged graphics processors to accelerate computations and provided insight into systems of both theoretical and practical importance. Dr. Jankowski began focusing on renewable energy generation during his postdoctoral positions at the University of Colorado and the National Renewable Energy Laboratory. At these postdocs\, Dr. Jankowski applied techniques he developed during his thesis to understand factors that determine the ordering of molecules in organic solar cells. \nThis is a joint seminar of the Scientific Computing Student Club and MICDE\, sponsored in part by U-M Rackham Graduate School.   \n  \nCobbling together computational components to engineer inexpensive plastic solar panels\nIn order to meet projected global energy demands over the next 25 years\, the equivalent of building a 1GW power plant each day is needed. Existing clean power generation technologies can meet this demand in principle\, but their relatively large short-term costs have limited widespread adoption. In this work we explain manufacturing strategies for organic (plastic) solar panels that overcome economic barriers to adoption by optimizing the structure of the organic active layer responsible for generating electricity. We perform coarse-grained molecular dynamics simulations accelerated with graphics processing units to determine the thermodynamically stable morphologies for a variety of candidate ingredients. Using these morphologies we perform kinetic Monte Carlo charge transport simulations to determine which morphologies are better candidates for solar devices. The simulation pipeline developed here combines computational tools developed for solving unrelated problems\, and we discuss the evolving landscape of scientific computing education and how it overlaps with this work. \n 
URL:https://micde.umich.edu/event/sc2micde-seminar-eric-jankowski-material-science-and-engineering-boise-state-university/
LOCATION:2540 G.G. Brown (2350 Hayward St.)\, 2300 Hayward St\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20170307T160000
DTEND;TZID=America/Detroit:20170307T170000
DTSTAMP:20260604T111332
CREATED:20230905T171438Z
LAST-MODIFIED:20230905T171438Z
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SUMMARY:MICDE Seminar: Michael Eldred\, Computation\, Computers\, Information\, and Mathematics Center\, Sandia National Laboratories
DESCRIPTION:Bio: Michael Eldred is a Distinguished Member of the Technical Staff in the Optimization and Uncertainty Quantification Department within the Computation\, Computers\, Information\, and Mathematics Center at Sandia National Laboratories. He received his B.S. in Aerospace Engineering from Virginia Tech in 1989\, his M.S.E. and Ph.D. in Aerospace Engineering from the University of Michigan in 1990 and 1993. Mike led the DAKOTA project\, a “… toolkit that provides a flexible\, extensible interface between analysis codes and iterative systems analysis methods…”\, for 15 years (1994-2009) and now leads algorithm research and development activities related to DAKOTA. Mike’s research interests include uncertainty quantification\, design under uncertainty\, surrogate-based optimization\, and high-performance computing\, with application to stockpile stewardship and energy initiatives through the NNSA ASC\, DOE ASCR\, and DOE SciDAC programs. \nMike is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) and a member of the Society for Industrial and Applied Mathematics (SIAM)\, the International Society for Structural and Multidisciplinary Optimization (ISSMO)\, and the United States Association for Computational Mechanics (USACM). He currently serves as a member of the AIAA Nondeterministic Approaches Technical Committee and on the editorial board for the International Journal for Uncertainty Quantification. A number of his publications are available on the DAKOTA web site. \nTitle: Multilevel-Multifidelity Approaches for Uncertainty Quantification and Design\nIn the simulation of complex physics\, multiple model forms of varying fidelity and resolution are commonly available. In computational fluid dynamics\, for example\, common model fidelities include potential flow\, inviscid Euler\, Reynolds-averaged Navier-Stokes\, and large eddy simulation\, which may be further augmented by subgrid-scale model selections and spatio-temporal discretization levels. In this presentation\, we focus on novel algorithms that simultaneously exploit multiple model forms and multiple resolutions\, both for uncertainty quantification (UQ) and for optimization under uncertainty (OUU). These hybrid methods exploit multifidelity methods across the model form hierarchy in combination with multilevel accelerators across an associated discretization hierarchy\, manifesting as multilevel control variate Monte Carlo and multilevel polynomial expansion methods in the UQ case and recursive trust-region and multigrid optimization in the OUU case. These techniques will be demonstrated for both model problems and engineered systems\, and will be placed within the broader context of algorithm research and development within the Dakota project at Sandia. \nDr. Eldred is being hosted by Prof. Duraisamy (Aerospace Engineering) 
URL:https://micde.umich.edu/event/micde-seminar-michael-eldredcomputation-computers-information-and-mathematics-center-sandia-national-laboratories/
LOCATION:1008 FXB\, 1320 Beal Ave\, Ann Arbor\, MI\, 48109
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20170214T150000
DTEND;TZID=America/Detroit:20170214T160000
DTSTAMP:20260604T111332
CREATED:20230905T171440Z
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SUMMARY:MICDE Seminar: Steven White\, Physics & Astronomy\, University of California Irvine
DESCRIPTION:Bio: Steven White did his bachelor’s degree at the University of California in San Diego and received his Ph.D. from Cornell University. Early in his career he was awarded a National Science Foundation fellowship\, and an IBM postdoctoral fellowship. He’s been named an American Physical Society fellow\, and a fellow of the American Association for the Advancement of Science\, and of the American Academy of Arts and Science\, among others. Professor White is most known for inventing the Density Matrix Renormalization Group (DMRG)\, a numerical variation technique for high accuracy calculations of the low energy physics of quantum many-body systems. In 2003 he won the American Physical Society Aneesur Rahman prize\, a recognition of outstanding achievement in computational physics research “…for his development\, application\, and dissemination of the DMRG method”. He has published over one hundred and seventy papers on this and related subjects. \nTensor Network methods for Electronic Structure\nOur conventional picture of wave functions living in an exponentially large Hilbert space is both impractical for solving many particle systems and conceptually lacking: in recent years we have come to understand that physical states of matter live in an infinitesimal corner of Hilbert space\, characterized primarily by low entanglement. Tensor networks are the natural language to express low entanglement wave functions\, giving an exponentially compressed description of ground states. The density matrix renormalization group (DMRG) and other tensor network algorithms have had tremendous success in simulating quantum lattice models.The key challenge in translating these methods to electronic structure is the need to represent continuum space in an efficient way. After an introduction to tensor networks\, I’ll present a new DMRG-based approach suitable for the electronic structure of long molecules. Our sliced-basis DMRG method produces near-exact ground states within its basis\, and has a computation time which is linear in the length of the molecule. We are implementing SBDMRG for chains of hydrogen atoms\, where we have been able to simulate up to 1000 atoms in a minimal basis. \nProf. White is being hosted by Prof. Emanuel Gull (Chemistry)
URL:https://micde.umich.edu/event/micde-seminar-steven-white-physics-astronomy-university-of-california-irvine/
LOCATION:340 West Hall\, 1085 South University Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20170203T100000
DTEND;TZID=America/Detroit:20170203T110000
DTSTAMP:20260604T111332
CREATED:20230905T171440Z
LAST-MODIFIED:20230905T171440Z
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SUMMARY:MICDE Seminar: Anna Krylov\, Chemistry\, University of Southern California
DESCRIPTION:Bio: Anna Krylov is a Gabilan Distinguished Professor in Science and Engineering\, Chemistry at the University of Southern California. She received her M.Sc. in Chemistry from Moscow State University and later her Ph.D. from The Hebrew University of Jerusalem. Upon completing her Ph.D. in 1996 (summa cum laude)\, she joined the group of Prof. Martin Head-Gordon at the University of California\, Berkeley as a postdoctoral research associate\, where she first became involved with electronic structure method development. In 1998\, she joined Department of Chemistry at USC. Currently\, Prof. Krylov leads a research group focused on theoretical modeling of open shell and electronically excited species. She is the head of the Center for Computational Studies of Electronic Structure and Spectroscopy of Open-Shell and Electronically Excited Species\, iOpenShell\, supported by the National Science Foundation (2005–2011) and the University of Southern California. She is developing robust black-box methods aiming to describe complicated multi-configurational wave functions in a single-reference formalism\, such as coupled-cluster and equation-of-motion (or linear response) approaches. She has developed the spin-flip approach\, which extends coupled-cluster and density functional methods to diradicals\, triradicals\, and bond-breaking. Using computational chemistry tools\, and in collaboration with numerous experimental groups\, Krylov is also investigating the role that radicals and electronically excited species play in such diverse areas as combustion\, gas- and condensed-phase chemistry\, solar energy applications\, bioimaging\, and ionization-induced processes in biology. She has co-authored more than 120 publications and has delivered more than 130 invited lectures. (Source https://en.wikipedia.org/wiki/Anna_Krylov) \nFission of entangled spins: Electronic structure perspective\nSinglet fission (SF)\, a process in which one singlet excited state is converted into two triplet states\, is of interest in the context of organic photovoltaic technology. Owing to its technological significance\, the mechanism of SF has been vigorously investigated. Yet\, the design principles for materials capable of efficient SF remain elusive. The main challenge faced by theory is a complex and intricate electronic structure of the process\, which involves non-adiabatic transitions between strongly correlated states. This lecture will discuss electronic structure of the relevant states\, the nature of non-adiabatic couplings\, and the connection between electronic factors and rates\, emphasizing the methodological aspects of the problem. The utility of theory will be illustrated by examples. Recent experimental and theoretical studies of SF in covalently linked tetracene dimers shed light on the effect of the linkers on the electronic factors and SF rates\, illuminating the role of through-space and through-bond interactions between the chromophores. The results highlight the importance of integrative approaches that evaluate the overall rate\, rather than focus on specific electronic factors\, such as energies or couplings.
URL:https://micde.umich.edu/event/micde-seminar-anna-krylov-chemistry-university-of-southern-california/
LOCATION:CHEM 1640\, 930 N University\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20170127T120000
DTEND;TZID=America/Detroit:20170127T130000
DTSTAMP:20260604T111332
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SUMMARY:MICDE Seminar: Vipin Kumar\, Computer Science and Engineering\, University of Minnesota
DESCRIPTION:Bio: Vipin Kumar is a Regents Professor and holds William Norris Chair in the department of Computer Science and Engineering  at the University of Minnesota.  His research interests include data mining\, high-performance computing\, and their applications in Climate/Ecosystems and health care. He is currently leading an NSF Expedition project on understanding climate change using data driven approaches.  He has authored over 300 research articles\, and co-edited or coauthored 10 books including the widely used text book “Introduction to Parallel Computing”\, and “Introduction to Data Mining”.  Kumar co-founded SIAM International Conference on Data Mining and served as a founding co-editor-in-chief of Journal of Statistical Analysis and Data Mining (an official journal of the American Statistical Association).  Kumar is a Fellow of the ACM\, IEEE and AAAS.  He received the Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Roorkee (2013) and the Distinguished Alumnus Award from the Computer Science Department\, University of Maryland College Park (2009).  Kumar’s foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award\, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD)\, and the 2016 IEEE Computer Society Sidney Fernbach Award\, one of IEEE Computer Society’s highest awards. \nBig Data in Climate: Opportunities and Challenges for Machine Learning and Data Mining\nThis talk will present an overview of research being done in a large interdisciplinary project on the development of novel data mining and machine learning approaches for analyzing massive amount of climate and ecosystem data now available from satellite and ground-based sensors\, and physics-based climate model simulations. These information-rich data sets offer huge potential for monitoring\, understanding\, and predicting the behavior of the Earth’s ecosystem and for advancing the science of global change. This talk will discuss challenges in analyzing such data sets and some of our research results in mapping the dynamics of surface water globally as well as detecting deforestation and fires in tropical forests using data from Earth observing satellites. \nResearch funded by the NSF Expeditions in Computing Program and  NASA \nPizza lunch will be provided
URL:https://micde.umich.edu/event/micde-seminar-vipin-kumar-computer-science-and-engineering-university-of-minnesota/
LOCATION:1008 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Featured Events,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20161202T140000
DTEND;TZID=America/Detroit:20161202T150000
DTSTAMP:20260604T111332
CREATED:20230905T171440Z
LAST-MODIFIED:20230905T171440Z
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SUMMARY:MICDE/RadLab/IEEE Seminar: Levent Gürel\, ABAKUS Computing Technologies
DESCRIPTION:Bio: Prof. Levent Gürel (Fellow of IEEE\, ACES\, and EMA) received the M.S. and Ph.D. degrees from the University of Illinois at Urbana-Champaign (UIUC) in 1988 and 1991\, respectively\, in electrical and computer engineering. He worked at the IBM Thomas J. Watson Research Center\, Yorktown Heights\, New York\, in 1991-94. During his 20 years with Bilkent University\, he served as the Founding Director of the Computational Electromagnetics Research Center (BiLCEM) and a professor of electrical engineering. He is also an Adjunct Professor at UIUC. Prof. Gürel is the Founder and CEO of ABAKUS Computing Technologies\, a company that is geared towards advancing the use of cutting-edge computing technologies for solving difficult scientific problems with important real-life applications and societal benefits. He is conferred the UIUC ECE Distinguished Alumni Award in 2013 and the IEEE Harrington-Mittra Award in Computational Electromagnetics in 2015. He is an IEEE Distinguished Lecturer. He was invited to address the 2011 and 2017 ACES Conferences as a Plenary Speaker and a TEDx Conference in 2014. Among other recognitions of Prof. Gürel’s accomplishments\, the two prestigious awards from the Turkish Academy of Sciences (TUBA) in 2002 and the Scientific and Technological Research Council of Turkey (TUBITAK) in 2003 are the most notable. Since 2003\, Prof. Gürel has been serving as an associate editor for Radio Science\, IEEE Transactions on Antennas and Propagation\, IEEE Antennas and Wireless Propagation Letters\, IET Microwaves\, Antennas & Propagation\, JEMWA\, PIER\, ACES Journal\, and ACES Express. \nSolution of Extremely Large Forward and Inverse Problems in Computational Electromagnetics: BIG DATA Aspects\nAs we solve some of the largest problems in the interdisciplinary domain of computational electromagnetics\, we have to deal with various aspects of big-data issues routinely. Most recently\, we have achieved the solutions of larger than 1\,500\,000\,000×1\,500\,000\,000 (1.5 billion!) dense matrix equations! This achievement is an outcome of a multidisciplinary effort involving physical understanding of electromagnetics problems\, novel parallelization strategies (computer science)\, constructing parallel clusters (computer architecture)\, advanced mathematical methods for integral equations\, fast solvers\, iterative methods\, preconditioners\, linear algebra\, and big data. Solving such large problems on a regular basis requires the generation\, representation\, storage\, processing\, analysis\, transfer and communication\, visualization and interpretation of extremely large data sets in the order of multiple terabytes. \nAccurate formulations of real-life electromagnetics problems with integral equations necessitate the solution of extremely large dense matrix equations. Solutions of such tremendously challenging problems cannot be achieved easily\, even when using the most powerful computers with state-of-the-art petascale computing capabilities. Instead\, we have been solving some of the world’s largest integral-equation problems in computational electromagnetics by employing fast algorithms implemented on parallel computers. To achieve optimal management of multiple large data sets\, we design and implement the handling of data in various levels of cache\, memory\, and disk\, leading to meticulously designed out-of-core (OoC) schemes. That way\, we enable the solution of unprecedentedly large problems with limited amounts of DRAM. In order to avoid decelerating the solution\, we optimize communications among CPU cores\, among processors\, among nodes\, from CPU to disk (and back)\, and in the case of heterogeneous architectures\, we carefully control the data traffic to/from GPUs. Furthermore\, we employ MPI and OpenMP simultaneously in a parallelization strategy designed to reduce data duplications among processes so that vast numbers of cores can be efficiently utilized without requiring extra memory. \nI will present fast and accurate solutions of large-scale electromagnetic forward and inverse problems involving three-dimensional geometries that are larger than 1000 wavelengths using the multilevel fast multipole algorithm (MLFMA) and parallel MLFMA. Solving the world’s largest computational electromagnetics problems has important implications in terms of obtaining the solutions of future grand-challenge problems in imaging\, (subsurface)\, optics\, nanotechnology\, bio-electromagnetics\, metamaterials\, remote sensing\, as well as plethora of other disciplines of science\, e.g.\, acoustics\, elastics\, quantum mechanics\, astrophysics\, molecular dynamics\, electro-statics\, fluid dynamics\, thermodynamics. For more information: http://captains.of.computing.technology/.
URL:https://micde.umich.edu/event/micderadlabieee-seminar-levent-gurel-abakus-computing-technologies/
LOCATION:3427 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Education,Featured Events,MICDE Seminar Series,Seminar
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