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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:20260317T160000
DTEND;TZID=America/Detroit:20260317T170000
DTSTAMP:20260605T090122
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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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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