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DTSTAMP:20260605T091938
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SUMMARY:Data Science/Computational Social Science/MICDE Seminar: Yian Ma\, Assistant Professor\, UC San Diego
DESCRIPTION:Bio:  Yian Ma is an assistant professor at the Halıcıoğlu Data Science Institute\, UC San Diego\, where he serves as the vice chair for the graduate programs. Prior to UCSD\, he spent a year as a visiting faculty at Google Research. Before that\, he was a post-doctoral fellow at UC Berkeley\, hosted by Mike Jordan. Yian completed his Ph.D. at the University of Washington. His current research primarily revolves around scalable inference methods for credible machine learning\, with application to time series data and sequential decision-making tasks. He has received the Facebook research award\, the Stein fellowship\, and the best paper awards at the Neurips and ICML workshops. \nMCMC\, variational inference\, and reverse diffusion Monte Carlo\nProf. Ma will introduce some recent progress toward understanding the scalability of Markov chain Monte Carlo (MCMC) methods and their comparative advantage with respect to variational inference. I will fact-check the folklore that “variational inference is fast but biased\, MCMC is unbiased but slow”. I will then discuss a combination of the two via reverse diffusion\, which holds promise of solving some of the multi-modal problems. This talk will be motivated by the need for Bayesian computation in reinforcement learning problems and the differential privacy requirements we face. \nRSVP HERE \n\n  \nThe MICDE Winter 2025 Seminar Series is open to all. University of Michigan faculty and students. \nThis is an in-person event. \nGraduate Certificate in Computational Discovery and Engineering\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/data-science-computational-social-science-micde-seminar-yian-ma-assistant-professor-uc-san-diego/
LOCATION:438 West Hall\, 1085 UNIVERSITY AVE\, Ann Arbor\, 48109\, United States
CATEGORIES:Data Science,Featured Events,Micde Seminar,MICDE Seminar Series
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DTSTART;TZID=America/Detroit:20241001T150000
DTEND;TZID=America/Detroit:20241001T160000
DTSTAMP:20260605T091938
CREATED:20240925T142215Z
LAST-MODIFIED:20241011T124325Z
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SUMMARY:MICDE/ME Seminar: Krishnan Mahesh\, Professor\, University of Michigan NAME
DESCRIPTION:Bio:  Krishnan Mahesh is a Richard B. Couch Professor of Naval Architecture and Marine Engineering at the University of Michigan. His research focuses on the simulation of complex\, multi-physics turbulent flows. Mahesh received his Bachelor’s degree in Mechanical Engineering from the Indian Institute of Technology (Mumbai)\, and in 1996 obtained his Ph.D. degree in Mechanical Engineering from Stanford University. He is a 2018 Fulbright-Nehru Specialist\, a Fellow of the American Physical Society\, an Associate Fellow of the American Institute of Aeronautics and Astronautics\, and a Fellow of the Minnesota Supercomputing Institute. Mahesh is a recipient of the CAREER Award from the National Science Foundation and the Francois N. Frenkiel Award from the American Physical Society. He has received the Taylor Award for Distinguished Research\, McKnight Presidential Fellowship\, Guillermo E. Borja Award\, and McKnight Land-Grant Professorship from the University of Minnesota. \nLarge Eddy Simulation of Turbulent Cavitating Flows\nCavitation is a complex multi-scale phenomenon that has implications from intense sound production to erosion in engineering applications. This talk will discuss our efforts at developing the large-eddy simulation capability for the simulation of turbulent cavitating flows. LES of cavitation is challenged by phase change modeling\, acoustic stiffness\, sharp multiphase fronts\, strong compressibility effects\, consistent accounting of nuclei\, broadband turbulence and subgrid effects. \nLES of partial cavitation will be discussed under the same conditions as experiments in a sharp wedge configuration.  Physical mechanisms of cavity transition observed in the experiments\, i.e.\, re-entrant jet and bubbly shock waves\, are both captured in the LES over their respective regimes. Vapor volume fraction data obtained from the LES will be quantitatively compared to X-ray densitometry\, and the results will be discussed. Cavitation nuclei are likely to be introduced through the free-stream as well as at solid surfaces. We will present a novel approach based on Gibbs free energy minimization to predict nuclei concentrations. The results from the proposed work will be applied to account for dissolved gas content in CSM measurements and predict several decades of experimentally observed trends in nuclei concentrations. Cavitating flows possess a range of vapor length scales ranging from tiny vapor bubbles to large vapor pockets. We will discuss a compressible hybrid model to capture both sub-grid vapor nuclei and massive sheet cavity dynamics. Finally\, physical aspects of inception due to the interaction of a counter–rotating vortex pair generated behind a pair of hydrofoils will be presented. \n\n  \nThe MICDE Fall 2025 Seminar Series is open to all. University of Michigan faculty and students. \nThis is an in-person event. \nGraduate Certificate in Computational Discovery and Engineering\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-me-seminar-krishnan-mahesh-professor-university-of-michigan-name/
LOCATION:2150 H.H. Dow\, 2300 Hayward St\, Ann Arbor\, 48109\, United States
CATEGORIES:Computational Science,Engineering,Featured Events,Free,Mechanical Engineering,Micde Seminar,MICDE Seminar Series,Michigan Engineering,Naval Architecture and Marine Engineering
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20241204T160000
DTEND;TZID=America/Detroit:20241204T170000
DTSTAMP:20260605T091938
CREATED:20241126T144049Z
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SUMMARY:MICDE-Aerospace Engineering Seminar: Jan Janssen\, Scientist\, Max Planck Institute
DESCRIPTION:Bio:\nDr. Jan Janssen is the group leader for materials informatics at the Max Planck Institute for Sustainable Materials in Düsseldorf\, Germany. Previously\, he was a Director’s Postdoctoral Fellow at Los Alamos National Laboratory\, where he designed materials for fusion reactors as part of the Exascale Computing Project. In addition\, he leads the development of the open-source pyiron software package\, is a maintainer of over 900 open-source software packages for the conda-forge community and an active contributor to open-source projects on Github. \nTitle:\nHow to use machine learning in the discovery and design of materials for the future? \nAbstract:\nDesigning materials for a sustainable future requires rethinking traditional materials design\, which is centered on optimizing and fine-tuning already known alloying compositions. In a mathematical sense this can be identified as a local or global optimization in the multi-dimensional alloy phase space. To sample the whole periodic table\, already a three-component alloy with 20 temperature steps and 10 concentration steps requires a million experiments\, making it prohibitive for purely experimental approaches.\nTo address this challenge\, simulation approaches and\, more recently\, machine learning models are applied to screen the periodic table. The pyiron workflow framework developed at the Max-Planck-Institute for sustainable materials predicts new materials using ab-intio thermodynamics. Starting from the interaction of electrons\, it predicts macroscopic material properties like heat capacity\, thermal expansion\, and phase stability. Recently\, the pyiron workflow framework was extended with a large language model (LLM) interface named LangSim.\nThis raises the question: Can a LLM replace a scientist? Or how does the thought process of a scientist differ from the statistical approach of the LLM? Can the LLM make us better scientists? We benchmark the capabilities of current LLMs to design new materials using atomistic simulation. The presentation introduces ab-initio thermodynamics\, covers the importance of simulation workflows to efficiently predict sustainable materials and highlights how LLMs accelerate their discovery.
URL:https://micde.umich.edu/event/workshop-seminarmicde-aerospace-engineering-seminar-jan-janssen-how-to-use-machine-learning-in-the-discovery-and-design-of-materials-for-the-future/
LOCATION:Cooley Building – 906
CATEGORIES:Aerospace Engineering,Micde,Micde Seminar,Michigan Engineering
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DTSTART;TZID=America/Detroit:20250117T160000
DTEND;TZID=America/Detroit:20250117T170000
DTSTAMP:20260605T091938
CREATED:20241224T044635Z
LAST-MODIFIED:20260522T182843Z
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SUMMARY:MICDE - NERS Seminar: Teresa Bailey\, Lawrence Livermore National Laboratory
DESCRIPTION:Bio: Teresa S. Bailey is the Associate Program Director of Computational Physics in LLNL’s Weapon Simulation and Computing program. She oversees the development of multiple multiphysics simulation tools across a wide range of applications. These codes span a broad range of physics\, chemistry\, and engineering application space. As required\, the codes are production-quality software products that are portable and computationally efficient on DOE’s most advanced HPC systems. \nBailey has been an LLNL employee since 2008. She began her career as a code physicist before moving into technical leadership roles as the Deterministic Transport project leader and the Nuclear Science program group leader. Bailey earned her B.S. in Nuclear Engineering from Oregon State University in 2002. She received the DOE Computational Science Graduate Fellowship to support her graduate work and earn her Ph.D. in Nuclear Engineering from Texas A&M in 2008. \nComputational Science and High-Performance Computing at Lawrence Livermore National Laboratory
URL:https://micde.umich.edu/event/workshop-seminarmicde-ners-seminar-teresa-bailey-lawrence-livermore-national-laboratory/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:College Of Engineering,Computational Science,Micde,Micde Seminar,Michigan Engineering,Nuclear Engineering and Radiological Sciences,Physics,Seminar
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DTSTART;TZID=America/Detroit:20250121T150000
DTEND;TZID=America/Detroit:20250121T160000
DTSTAMP:20260605T091938
CREATED:20250106T213237Z
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SUMMARY:MICDE-CSE Seminar: Andrew Appel\, Professor\, Princeton University
DESCRIPTION:Bio: Andrew Appel is Eugene Higgins Professor Computer Science\, and served from 2009-2015 as Chair of Princeton’s CS department. His research is in software verification\, computer security\, programming languages and compilers\, and technology policy. He received his A.B. summa cum laude in physics from Princeton in 1981\, and his Ph.D. in computer science from Carnegie Mellon University in 1985. Professor Appel has been editor in chief of ACM Transactions on Programming Languages and Systems and is a fellow of the ACM (Association for Computing Machinery). He has worked on fast N-body algorithms (1980s)\, Standard ML of New Jersey (1990s)\, Foundational Proof-Carrying Code (2000s)\, and the Verified Software Toolchain (2010-present). \nFormally Verified Numerical Methods\nAbstract: Formal machine-checked program verification uses mechanized logical tools to connect low-level programs to the specifications of the algorithms they are supposed to implement. The same program verification tools can work in many application domains. But it’s not enough just to implement an algorithm; the program is fully “correct” only if the algorithm (provably) computes an answer to the problem or question of interest. Proofs of algorithm correctness rely on the mathematics of the application domains\, and each domain has its own mathematics.\nIn recent years we have applied this method to numerical methods (algorithms for scientific computing) and numerical analysis (reasoning about the accuracy of those methods)\, with machine-checked proofs formally connected to low-level program-correctness proofs. I will discuss the results of the numerical integration of differential equations and the solving of linear systems. Some of these results are joint work with Ariel Kellison and David Bindel (Cornell)\, Mohit Tekriwal and Jean-Baptiste Jeannin (Michigan).
URL:https://micde.umich.edu/event/micde-cse-seminar-andrew-appel-professor-princeton-university/
LOCATION:BBB 3725\, 2260 Hayward St.\, Ann Arbor\, United States
CATEGORIES:Micde,Micde Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250128T160000
DTEND;TZID=America/Detroit:20250128T170000
DTSTAMP:20260605T091938
CREATED:20241011T222157Z
LAST-MODIFIED:20260522T151639Z
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SUMMARY:MICDE Seminar: Joshua Dolence\, Research Scientist\, Los Alamos National Lab
DESCRIPTION:Bio: Josh Dolence is a scientist in the Computational Physics & Methods Group at Los Alamos National Laboratory and a founding member of the LANL Michigan SPARC\, a permanent LANL presence at U-M in Ann Arbor. Before joining LANL\, he received a PhD in Astronomy from UIUC in 2011 and spent three years in Astrophysical Sciences at Princeton University where he worked in computational astrophysics\, studying topics like black hole accretion and supernovae. More recently\, he leads the Methods for Multiscale\, Multiphysics Accelerated Prediction project for LANL’s Advanced Simulation and Computing Program\, focusing efforts on enabling unprecedented fidelity and scale in modeling complex systems like high energy density physics experiments. \nParthenon: a flexible framework for rapid development of performance portable multiphysics codes\nAbstract: In many areas of computational science\, developing new\, state-of-the-art capabilities has become a high-cost\, risky proposition. The complexity and diversity of models\, methods\, algorithms\, and machines often lead to fundamental challenges in designing and building codes that enable advances in science and engineering. In fields like high energy density physics and astrophysics\, multiphysics simulations leveraging adaptive meshes\, particles\, and a variety of numerical methods are foundational to progress but difficult to realize performantly on ever-evolving high-performance computing platforms. In this talk\, I will present the Parthenon framework\, an open-source code base that aims to facilitate the development of highly adaptive\, multiphysics codes that are fast\, scalable\, and capable of leveraging modern platforms with both CPUs and GPUs. I will describe the basic principles behind its design and some of its most enabling features and highlight the ~10 downstream codes it already supports. \n 
URL:https://micde.umich.edu/event/joshua-dolence/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Micde,Micde Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250206T150000
DTEND;TZID=America/Detroit:20250206T160000
DTSTAMP:20260605T091938
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SUMMARY:MICDE-IOE Seminar: Jong-Shi Pang\, Professor\, University of Southern California
DESCRIPTION:Bio: Elected a member of the National Academy of Engineering in February 2021 and appointed a Distinguished Professor in April 2023\, Jong-Shi Pang joined the University of Southern California as the Epstein Family Chair and Professor of Industrial and Systems Engineering in August 2013. Prior to this position\, he was the Caterpillar Professor and Head of the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champagne for six years between 2007 and 2013. He held the position of the Margaret A. Darrin Distinguished Professor in Applied Mathematics in the Department of Mathematical Sciences and was a Professor of Decision Sciences and Engineering Systems at Rensselaer Polytechnic Institute from 2003 to 2007. He was a Professor in the Department of Mathematical Sciences at the Johns Hopkins University from 1987 to 2003\, an Associate Professor and then Professor in the School of Management from 1982 to 1987 at the University of Texas at Dallas\, and an Assistant and then an Associate Professor in the Graduate School of Industrial Administration at Carnegie-Mellon University from 1977 to 1982. During 1999 and 2001 (full-time) and 2002 (part-time)\, he was a Program Director in the Division of Mathematical Sciences at the National Science Foundation. Professor Pang has served as the Department Academic Advisor of the Department of Mathematics at the Hong Kong Polytechnic University. He has given many distinguished lectures at universities worldwide and plenary lectures at international conferences. \nHeaviside Composite Optimization\, a new paradigm of optimization\nAbstract: This talk introduces the topic of Heaviside composite optimization and briefly covers its many facets: breadth in modeling\, roles in old and new applications\, theory of optimizers and stationary solutions\, bridge with discrete optimization\, and the progressive integer programming method. By definition\, a univariate Heaviside function is the (discontinuous) indicator of an interval. By its name\, a Heaviside composite function is the composition of a Heaviside function with a continuous multivariate function that may be nonconvex and nondifferentiable. While very natural in modeling many physical phenomena\, a Heaviside composite optimization problem\, possibly with Heaviside composite functional constraints\, has never been formally studied. Our work aims to fill this void with a comprehensive research program covering the applications\, theory\, and algorithms for this novel class of very challenging optimization problems. \nThis research has benefitted from previous collaboration with Ying Cui (UC Berkeley)\, Yue Fan (CUHK-SZ)\, Shaoning Han (NUS)\, Junyi Liu (Tsinghua)\, and Xinyao Zhang (USC)\, and is presently being organized in a monograph co-authored with Junyi Lui.
URL:https://micde.umich.edu/event/jong-shi-pang/
LOCATION:Johnson Rooms\, Lurie Engineering Center\, 3rd Floor LEC 3213ABC\, 1221 Beal Ave.\, Ann Arbor\, MI\, United States
CATEGORIES:Micde,Micde Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20250320T153000
DTEND;TZID=America/Detroit:20250320T163000
DTSTAMP:20260605T091938
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SUMMARY:MICDE-EECS Seminar: Mikhail Belkin\, Professor\, University of California San Diego
DESCRIPTION:Bio: Mikhail Belkin is a Professor at Halicioglu Data Science Institute and Computer Science and Engineering Department at UCSD and an Amazon Scholar. Prior to that he was a Professor at the Department of Computer Science and Engineering and the Department of Statistics at the Ohio State University. He received his Ph.D. from the Department of Mathematics at the University of Chicago (advised by Partha Niyogi). His research interests are broadly in theory and applications of machine learning\, deep learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps\, Graph Regularization and Manifold Regularization algorithms\, which brought ideas from classical differential geometry and spectral graph theory to data science. His more recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. The empirical evidence necessitated revisiting some of the classical concepts in statistics and optimization\, including the basic notion of over-fitting. One of his key findings has been the “double descent” risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation. His recent work focusses on understanding feature learning and over-parameterization in deep learning. Mikhail Belkin is an ACM Fellow and a recipient of a NSF Career Award and a number of best paper and other awards. He had served on the editorial boards of IEEE Proceedings on Pattern Analysis Machine Intelligence and the Journal of the Machine Learning Research. He is the editor-in-chief of SIAM Journal on Mathematics of Data Science (SIMODS). \nEmergence and grokking in “simple” architectures\nAbstract: In recent years\, transformers have become a dominant machine learning methodology.\nA key element of transformer architectures is a standard neural network (MLP). I argue that MLPs alone already exhibit many remarkable behaviors observed in modern LLMs\, including emergent phenomena. Furthermore\, despite large amounts of work\, we are still far from understanding how 2-layer MLPs learn relatively simple problems\, such as “grokking” modular arithmetic. I will discuss recent progress and argue that feature-learning kernel machines (Recursive Feature Machines) isolate some key computational aspects of modern neural architectures and are preferable to MLPs as a model for analysis of emergent phenomena.
URL:https://micde.umich.edu/event/mikhail-belkin/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Micde,Micde Seminar
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DTSTART;TZID=America/Detroit:20250924T150000
DTEND;TZID=America/Detroit:20250924T170000
DTSTAMP:20260605T091938
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SUMMARY:MICDE Nobel Prize Lectures
DESCRIPTION:Speakers:\n\nCharles Brooks\, Warner-Lambert/Parke-Davis Professor of Chemistry\, Cyrus Levinthal Distinguished University Professor of Chemistry and Biophysics\, will talk about the 2024 Nobel Prizes in Chemistry.\nVeera Sundararaghavan\, Professor of Aerospace Engineering and the director of Multiscale Structural Simulations Laboratory\, will talk about the 2024 Nobel Prizes in Physics.\n\nNobel Prize Lectures\nThe 2024 Nobel Prizes in Physics and Chemistry spotlight the reciprocal influence between artificial intelligence and the natural sciences. This MICDE special event examines the science and scientists recognized for foundational advances in neural networks that underpin modern machine learning (Physics)\, and for AI-enabled breakthroughs in protein structure prediction and computational protein design (Chemistry). The lectures will be followed by a moderated panel and an open\, cross-disciplinary discussion. \nPanel Discussion:\nThe panel discussion\, followed by the lectures\, will address questions such as: What can AI do for science? How can it support existing ideas and create new ones? What can science do for AI? \nPanelists:\n\nJames Wells\, Professor of Physics\, University of Michigan\nIndika Rajapakse\, Professor of Computational Medicine and Bioinformatics\, and Professor of Mathematics\, University of Michigan\nCharles Brooks\, Warner-Lambert/Parke-Davis Professor of Chemistry\, Cyrus Levinthal Distinguished University Professor of Chemistry and Biophysics\nVeera Sundararaghavan\, Professor of Aerospace Engineering and the director of Multiscale Structural Simulations Laboratory\n\nModerator:\n\nKarthik Duraisamy\, Professor of Aerospace Engineering\, Mechanical Engineering and Nuclear Engineering and Radiological Sciences and Samir and Puja Kaul Director of the Michigan Institute for Computational Discovery and Engineering
URL:https://micde.umich.edu/event/nobel-prize-lecture/
LOCATION:Forum Hall\, Palmer Commons\, 100 Washtenaw Ave\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Chemistry,College Of Engineering,Computation,Computational Modeling,Computational Science,computing,Engineering,Featured Events,Free,Generative Ai,Graduate,Graduate and Professional Students,Graduate Students,Lecture,Machine Learning,Micde,Micde Seminar,Physics
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DTSTART;TZID=America/Detroit:20251002T140000
DTEND;TZID=America/Detroit:20251002T150000
DTSTAMP:20260605T091938
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SUMMARY:MICDE - MSE Seminar: Michael Herbst\, Swiss Federal Institute of Technology in Lausanne
DESCRIPTION:Bio: Michael Herbst obtained a PhD in Theoretical Chemistry from Heidelberg University in 2018\, after which he moved on to two postdoctoral research stays in Applied Mathematics with Éric Cancès (École des Ponts\, France) and Benjamin Stamm (RWTH Aachen\, Germany). Since March 2023\, he has been a tenure-track assistant professor in the Institute of Mathematics and the Institute of Materials at EPFL. His current research spans broadly in the field of materials simulations concerning numerical error control and uncertainty quantification of first-principle simulations\, as well as the propagation of such errors during inverse materials design or when training machine learning models. \nAlgorithmic differentiation (AD) for plane-wave DFT\nAbstract: Reliable algorithmic differentiation techniques offer great promise for the inverse design of materials and functionals\, as well as the propagating uncertainties from functionals to DFT quantities of interest. Over the past years\, considerable effort has been spent on equipping the density-functional toolkit (DFTK\, https://dftk.org) with algorithmic differentiation capabilities. Prof. Herbst will present some of the required algorithmic developments\, e.g. to efficiently compute such DFT derivatives in numerically challenging metallic systems. Furthermore\, he will highlight the conceptual difficulties associated with applying AD to plane-wave DFT and discuss our recent results\, which demonstrate the current state of AD in DFTK for error estimation\, inverse design\, and implementing new functionality. \nRead more
URL:https://micde.umich.edu/event/micde-seminar-michael-herbst/
LOCATION:1670 Bob and Betty Beyster Building\, 2260 Hayward Street\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Micde,Micde Seminar
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BEGIN:VEVENT
DTSTART;TZID=America/Detroit:20260129T140000
DTEND;TZID=America/Detroit:20260129T150000
DTSTAMP:20260605T091938
CREATED:20251125T210910Z
LAST-MODIFIED:20260522T151806Z
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SUMMARY:MICDE - Mechanical Engineering Seminar - Elif Ertekin\, University of Illinois Urbana-Champaign
DESCRIPTION:Bio: Elif Ertekin is an Andersen Faculty Scholar\, Associate Professor\, and Associate Head for Graduate Programs in the Mechanical Science and Engineering Department at the University of Illinois at Urbana-Champaign. She is a faculty affiliate of the National Center for Supercomputing Applications (NCSA) and the Materials Research Laboratory (MRL). Her research interests center on the theory and modeling of materials\, with an emphasis on probabilistic and stochastic methods. She focuses on developing a microscopic understanding of atomic and electronic scale processes in materials\, with applications areas in thermal transport\, energy conversion\, and defect chemistry. She received BS degrees in Mathematics and in Engineering Science and Mechanics from Penn State\, a PhD in Materials Science and Engineering from UC Berkeley\, and she carried out post-doctoral work at the Berkeley Nanoscience and Nanoengineering Institute and the Massachusetts Institute of Technology. She is an Associate Editor for the Journal of Applied Physics and a Divisional Associate Editor for\nPhysical Review Letters. \nPhysical Mechanisms or Learned Patterns? Reconciling First-Principles Models with Machine Learning for Predictive Materials\nPredictive materials simulation has long been rooted in first-principles descriptions of physical mechanisms\, grounded in quantum mechanics but limited by tractable length scales\, sampling challenges\, and the accuracy-cost tradeoff. Today\, machine-learning methods seek to transform materials science by revealing patterns in data extending beyond conventional modeling. My talk will explore how these two paradigms\, mechanistic simulation and data-driven learning\, can act synergistically to accelerate materials discovery and understanding. I will begin by outlining what first-principles simulations can currently achieve and where their limitations arise\, using examples from our work in thermoelectrics\, wide-band-gap semiconductors\, ion-transport materials\, and structural alloys. Building on this foundation\, I will show how machine-learning approaches\, when designed with materials-specific considerations such as symmetries and invariances\, can enhance traditional methods. Examples include symmetry-aware generative models for inorganic crystalline solids and machine-learning solutions to the many-body electronic-structure problem that rival high-accuracy quantum methods. Together\, these examples highlight how integrating mechanisms and patterns can help advance predictive materials simulations.\ \n\nThe MICDE 2025-26 Seminar Series is open to all. \nThis seminar is organized by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Mechanical Engineering. Prof. Ertekin will be hosted by Prof. Chenhui Shao\, Associate Professor of Mechanical Engineering. \nThis is an in-person event. This seminar will not be recorded! \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-seminar-elif-ertekin-uiuc/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms (LEC 3213)
CATEGORIES:College Of Engineering,Featured Events,Mechanical Engineering,Micde,Micde Seminar,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20260210T150000
DTEND;TZID=America/Detroit:20260210T160000
DTSTAMP:20260605T091938
CREATED:20260127T154702Z
LAST-MODIFIED:20260128T143051Z
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SUMMARY:MICDE - NERS - MIPSE Joint Seminar: Brian Haines\, Los Alamos National Laboratory
DESCRIPTION:Bio: Brian M. Haines is a Senior Distinguished Scientist in the Eulerian Codes group in the X-Computational Physics division at Los Alamos National Laboratory. He is currently the lead for the Ignition Applications project\, which includes the THOR and BrassOwl experimental campaigns on the National Ignition Facility. Brian leads the effort to produce LANL xRAGE pre-shot predictions and post-shot analysis of high-yield implosion attempts on the National Ignition Facility. Brian led the decadal effort to develop the xRAGE radiation-hydrodynamics code into a state-of-the-art tool for modeling inertial confinement fusion (ICF) and high-energy density physics experiments and has pioneered the use of xRAGE to perform large-scale high-resolution full-physics three-dimensional simulations of ICF implosions to understand the impacts of hydrodynamic instabilities and engineering features. Prior to his current position\, Brian was a Metropolis postdoc in the Methods & Algorithms group from 2011-2013 and did various internships as a student with Argonne National Laboratory\, LANL\, the National Security Agency\, and the Institute for Defense Analyses’ Center for Communications Research. Brian received a Ph.D. in mathematics from Penn State University in 2011 and a B.A. in mathematics and physics from New York University in 2006. Brian has co-authored 100 peer-reviewed publications that have received over 3\,400 citations and has been awarded a Secretary’s Honor Award from DOE\, four distinguished performance awards from LANL\, five defense program awards of excellence from NNSA\, an ICF program award from Lawrence Livermore National Laboratory (LLNL)\, and a Director’s Science and Technology Award from LLNL. \n  \nRadiation-hydrodynamics Modeling & Application to Prediction of Inertial Confinement Fusion Experiments\nThe xRAGE radiation-hydrodynamics code is a state-of-the art simulation tool for modeling inertial confinement fusion experiments. xRAGE is one of only three radiation-hydrodynamics codes developed in the U.S. with sufficient physics to credibly model both capsule implosions as well as the high-Z cylindrical hohlraums used to convert laser energy into an X-ray drive for the capsule. xRAGE solves the equations for hydrodynamics and other physics in an Eulerian reference frame and features adaptive mesh refinement\, which makes it uniquely well-suited to accurately modeling capsule defects and engineering features that are important factors limiting capsule performance. In the first half of this talk\, we will discuss the physics modeling capabilities and algorithms available in xRAGE with an emphasis on those relevant to high-energy-density physics and inertial confinement fusion. In the second half of the talk\, we will discuss the successful application of xRAGE to provide pre-shot predictions for seventeen high-yield capsule implosions on the National Ignition Facility. This will include the modeling methodology\, how we establish prediction uncertainties\, and how we have learned from prediction failures to improve the methodology. Our predictions have exhibited a 67% success rate thus far\, which is much higher than other pre-shot predictions over the same set of experiments. \n  \n\n  \nThe MICDE 2025-26 Seminar Series is open to all. \nThis seminar is organized by the Michigan Institute for Computational Discovery & Engineering (MICDE)\, the Department of Nuclear Engineering & Radiological Sciences (NERS) and the Michigan Institute for Plasma Science and Engineering (MIPSE). \nThis is an in-person event. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/brian-haines-los-alamos-national-laboratory/
LOCATION:Lurie Robert H. Engin. Ctr – Johnson Rooms (LEC 3213)
CATEGORIES:College Of Engineering,Featured Events,Micde,Micde Seminar,MICDE Seminar Series,Nuclear Engineering and Radiological Sciences,Seminar
ATTACH;FMTTYPE=image/jpeg:https://micde.umich.edu/wp-content/uploads/2026/01/Haines.jpg
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DTSTART;TZID=America/Detroit:20260317T160000
DTEND;TZID=America/Detroit:20260317T170000
DTSTAMP:20260605T091938
CREATED:20260306T144640Z
LAST-MODIFIED:20260306T144640Z
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SUMMARY:Mathematics - MICDE - MCAIM joint colloquium: Peter Bosler\, Sandia National Laboratories
DESCRIPTION:Bio:  Dr. Bosler received his B.S. degree with Honors in Oceanography from the U.S. Naval Academy in 2002. In 2002-2007\, he served as an officer in the U.S. Navy with active duty service that included both surface warfare and meteorology/oceanography operational support. Upon completing his service\, he started graduate studies at the University of Michigan and received a Ph.D. degree in Applied and Interdisciplinary Mathematics in 2013. In 2014\, he received the John von Neumann Postdoctoral Fellowship at Sandia National Laboratories\, and thereafter\, he became a staff member in the Center for Computing Research at Sandia. His projects involve close coupling between numerical methods development\, data collection\, application science\, and high-performance computing. Recent projects focus on climate modeling and plasma physics. Dr. Bosler received the Department of Energy Early Career Award for Advanced Scientific Computing in 2022 and the Presidential Early Career Award for Science and Engineering in 2025. \nAccelerating Earth System Simulation\nAbstract: Providing high-quality “actionable information” for strategic risk analysis is amongst the primary goals of the U.S. Department of Energy’s Exascale Earth System Model (E3SM). The simulation speed required to generate high-quality localized predictions at seasonal-to-decadal time scales is very high. In this talk\, we highlight some algorithmic design decisions that combine new research with classical numerical methods to enable E3SM’s ultra-high resolution configuration to achieve exascale performance and win the inaugural Gordon Bell Prize for Climate in 2023. Our design strategies tailor mathematical methods to both the unique features of the application space and to the heterogeneous computing architectures of exascale supercomputers. Ultimately\, these efforts doubled the speed of the most computationally demanding component of E3SM\, its atmosphere model. We will also discuss new and ongoing research associated with opportunities afforded by these performance gains. \n  \n\n  \nThe MICDE 2025-26 Seminar Series is open to all. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/math-micde-mcaim-peter-bosler-sandia/
LOCATION:1360 East Hall\, 530 Church St.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:Climate and Space Sciences and Engineering,College Of Engineering,Featured Events,Mathematics,Mechanical Engineering,Micde,Micde Seminar,MICDE Seminar Series,Seminar
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DTSTART;TZID=America/Detroit:20260529T110000
DTEND;TZID=America/Detroit:20260529T120000
DTSTAMP:20260605T091938
CREATED:20260514T175620Z
LAST-MODIFIED:20260601T195905Z
UID:10000862-1780052400-1780056000@micde.umich.edu
SUMMARY:MICDE - Mechanical Engineering seminar: Phani Motamarri\, Indian Institute of Science\, Bangalore
DESCRIPTION:Bio: Phani Motamarri is an Assistant Professor in the Department of Computational and Data Sciences at the Indian Institute of Science\, Bengaluru\, where he leads the MATRIX Lab. He is an alumnus of the University of Michigan–Ann Arbor\, where he earned his PhD in Mechanical Engineering.\nHis research lies at the intersection of computational mechanics\, materials science\, numerical analysis\, and high-performance computing. His work focuses on developing mathematical techniques and hardware-aware algorithms for quantum modeling of materials\, with applications to structural and functional materials and multiscale modeling methodologies. He is also interested in machine learning frameworks for accelerating materials discovery and quantum computing\, particularly in the context of quantum-centric supercomputing. \nProf. Motamarri’s research contributions include advances in finite-element methods\, numerical analysis\, and large-scale scientific software development. He is one of the lead developers of DFT-FE\, an open-source\, massively parallel finite-element code for density functional theory calculations. He received the ACM Gordon Bell Prize in 2023 and was a finalist for the ACM Gordon Bell Prize in 2019. \nInexact yet Accurate: Unlocking Quantum Modeling of Materials at Scale through Approximation-Tolerant Algorithms\nAbstract:  Modern computing architectures increasingly rely on iterative solvers that employ reduced-precision computation and communication-reduction techniques to lower time-to-solution and improve scalability. However\, eigensolvers in scientific simulations have struggled to exploit such approximations without compromising accuracy. We present an eigensolver R-ChFSI\, a residual-based reformulation of Chebyshev Filtered Subspace Iteration (ChFSI) provably tolerant to inexact matrix–vector products. By expressing the Chebyshev recurrence in terms of residuals rather than eigenvector estimates\, R-ChFSI naturally accommodates multiple sources of approximation\, including reduced-precision arithmetic (FP32 and TF32) in the filtering step\, lossy compression with compression ratios exceeding 4x for inter-process communication\, and approximate inverses for generalized eigenproblems\, while preserving eigensolver robustness. Large-scale experiments on GPU accelerators are conducted using finite-element discretized generalized eigenproblems arising in Kohn–Sham density functional theory for quantum modeling of materials. The results demonstrate that R-ChFSI achieves eigen-residual norms orders of magnitude smaller than standard ChFSI under comparable inexactness\, while delivering substantial performance gains. This work provides a practical pathway toward approximation-tolerant eigensolvers enabling accurate and scalable simulations on modern computing architectures. \n\nThe MICDE 2025-26 Seminar Series is open to all. \nGraduate Certificate in Computational Discovery and Engineering\, and MICDE fellows\, please use this form to record your attendance. \nQuestions? Email MICDE-events@umich.edu
URL:https://micde.umich.edu/event/micde-me-seminar-phani-motamarri-iisc/
LOCATION:1311 EECS\, 1301 Beal Ave.\, Ann Arbor\, MI\, 48109\, United States
CATEGORIES:College Of Engineering,Computational Science,Featured Events,Graduate Students,Mechanical Engineering,Micde,Micde Seminar,MICDE Seminar Series,Seminar
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