PhD Seminar: Pei-Hsun Huang
2023-02-23 16:00 - 16:30 (America/Detroit) | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speaker:
Pei-Hsun Huang, PhD Candidate, Nuclear Engineering & Radiological Sciences and Scientific Computing
Pei-Hsun is a PhD student in Nuclear Engineering & Radiological Sciences working with Professor Annalisa Manera in the Experimental and Computational Multiphase Flow Laboratory. His project involves high-temperature two-phase heat pipe technologies.
Simulation for the Design of Sodium Heat Pipes Bundle Test Facility for the Application of Microreactors
The 20 MW Special Purpose Reactor (SPR) is a heat pipe cooled microreactor that designed for electricity production in remote locations where reliable power grids are not always available. The key to SPR is the alkali metal heat pipes, which offer entirely passive operation capacity with high mobility. Prior to deployment, safety analysis with postulated accident scenarios is required for the licensing of SPR. To this regard, a sufficiently accurate model is crucial to predict the behavior of heat pipes, and high-resolution data is needed for the safety analysis of SPR. However, the current existing heat pipe models are either oversimplified or unpractical expensive in view of the difficulty of the simulation with the wick structure and two-phase flow in the heat pipe. Therefore, high fidelity experimental data is required for model verification in the high temperature heat pipe bundle system. The Michigan Sodium Heat Pipe bundle test facility which serves as a scale-down test facility using ten sodium heat pipes with a triangular array, was utilized to verify the model for the licensing of SPR. In the talk, the feasibility analysis using Computer Aided Engineering and Computational Fluid Dynamics for the design of the test facility was addressed.

This event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

PhD Seminar: Shirlyn Wang
2023-02-23 16:30 - 17:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speaker:
Shirlyn Wang, PhD Candidate, Applied & Interdisciplinary Mathematics and Scientific Computing
Shirlyn Wang is a Ph.D candidate in Applied and Interdisciplinary Mathematics. Her research interest is in mathematical oncology, the study of cancer initiation, progression, and therapy through data-driven mathematical models and simulations.
Modeling CTL-mediated Tumor Cell Death Mechanisms and the Activity of Immune Checkpoints in Immunotherapy
Immunotherapy has dramatically transformed the cancer treatment landscape. Of the variety of types of immunotherapies available, immune checkpoint inhibitors (ICIs), which block inhibitory signals from tumor cells and reinvigorate killing activities of immune cells, have gained the spotlight. Although ICIs have shown promising results for some patients, the low response rates in many cancers highlight the challenges of using immune checkpoint blockade as an effective treatment. Cytotoxic T lymphocytes (CTLs) execute their cell-killing function via two distinct mechanisms. The first process is fast-acting and perforin/granzyme-mediated, and the second is a slower, Fas ligand (FasL)-driven killing mechanism. There is also evidence suggesting that the preferred killing mechanism by CTLs depends on the antigenicity of tumor cells. To determine the key factors affecting responses to checkpoint blockade therapy, we constructed an ordinary differential equation model describing in vivo tumor-immune dynamics in the presence of active or blocked PD-1/PDL1 immune checkpoint. Specifically, we analyzed which aspects of the tumor-immune landscape affect the response to ICIs with endpoints of tumor size and composition in the short and long term. By generating a virtual cohort with heterogeneous tumor and immune attributes, we also simulated the therapeutic outcomes of immune checkpoint blockade in a largely diverse population. In this way, we identified key tumor and immune characteristics that are associated with tumor elimination, dormancy and escape. This talk will also shed light on which fraction of a population potentially responds well to ICIs and ways to enhance therapeutic outcomes with combination therapy.

This event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Daniele Schiavazzi, Associate Professor of Applied and Computational Mathematics and Statistics, University of Notre Dame
2023-03-02 15:30 - 16:30 | 1014 H. H. Dow, 2300 Hayward St, Ann Arbor, MI, 48109, United States
Daniele Schiavazzi is an Associate Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame. He graduated with honors and received a Ph.D. in Applied Mathematics from Universita’ degli Studi di Padova in Italy. He held postdoctoral appointments at the University of California, San Diego and Stanford University. He is the recipient of a CAREER Award from the National Science Foundation, a Young Faculty Award from DARPA and a Postdoctoral Fellowship from the American Heart Association. His research interests include uncertainty quantification, cardiovascular simulation, multi-resolution and multi-fidelity approximation, model-based inference and inverse problems in medical imaging.
Talk Title: NEW PARADIGMS FOR ENSEMBLE MODELING, UNCERTAINTY QUANTIFICATION AND INFERENCE IN CARDIOVASCULAR SIMULATION
Abstract:
Computer simulations are increasingly used to complement clinical decision making in the diagnosis and treatment of cardiovascular disease. High-fidelity cardiovascular models are traditionally deterministic and solved using implicit time integration, without directly accounting for uncertainty and variability in the underlying input processes, for example boundary conditions, material properties or segmented model anatomy. I will discuss an alternative simulation paradigm based on the explicit integration in time of an ensemble of model realizations, running on multiple GPUs. Additionally, I will present some results on the acceleration of traditional numerical solvers through data-driven methods based on deep neural networks, focusing on synchronization-avoiding algorithms for distributed finite element solvers. I will finally discuss recently proposed approaches for multi-fidelity uncertainty propagation and variational inference, combining high-fidelity cardiovascular models with their low-fidelity approximation or neural network surrogate.

The MICDE Fall 2022 Seminar Series is open to all.
This seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Schiavazzi will be hosted by Prof. Alex Gorodetsky, Assistant Professor of Aerospace Engineering.
This is an in-person event, Zoom link will only be provided upon request. This seminar will not be recorded!
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

PhD Seminar: Kashvi Srivastava
2023-03-09 16:00 - 16:30 | 3530 Rackham, 915 E. Washington St., Ann Arbor, MI, 48109, United States
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speaker:
Kashvi Srivastava, PhD Candidate, Applied and Interdisciplinary Mathematics and Scientific Computing
Kashvi Srivastava is a PhD candidate in Applied and Interdisciplinary Mathematics. Her research interests lie in the applications of nonlinear dynamics in chemical kinetics. She works on analytical and computational modeling of chemical reactions using tools from perturbation theory and bifurcation theory.
Deterministic and Stochastic Modeling of Dynamical Systems in Chemical Kinetics
Chemical reactions are ubiquitous in nature in the form of biological and physical processes. We use nonlinear ordinary differential equations to mathematically model these processes in the deterministic regime. If a given process occurs at disparate time-scales, we can further reduce the number of equations to obtain a quasi-steady-state approximation of the system. In this talk, we consider a significant mechanism in chemical kinetics called the Michaelis–Menten reaction and its different quasi-steady-state reductions. We focus on the challenges faced in applying classical reduction theory on the system and the conditions under which its reductions are valid in the stochastic regime. We make use of a stochastic simulation algorithm called the Gillespie algorithm to demonstrate the accuracy of the reduced systems and to disprove a commonly-accepted qualifier for the validity of the stochastic approximation.

This event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

PhD Seminar: Jiahao Shi
2023-03-09 16:30 - 17:00 | Venue TBA, MI, 48109, United States
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speaker:
Jiahao Shi, PhD Candidate, Industrial and Operations Engineering and Scientific Computing
He is from industrial and Operations Engineering department and is working on constrained stochastic optimization.
Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction
We propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically, we develop a method based on the sequential quadratic programming paradigm that employs variance reduction in the gradient approximations. Under reasonable assumptions, we prove that a measure of first-order stationarity evaluated at the iterates generated by our proposed algorithm converges to zero in expectation from arbitrary starting points, for both constant and adaptive step size strategies. Finally, we demonstrate the practical performance of our proposed algorithm on constrained binary classification problems that arise in machine learning.

This event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

PhD Seminar: Xingmin Wang
2023-03-16 16:00 - 16:30 | Venue TBA, MI, 48109, United States
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speaker:
Xingmin Wang, PhD Candidate, Civil and Environmental Engineering and Scientific Computing
Xingmin Wang is currently a Ph.D. candidate in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor, advised by Professor Henry Liu. He obtained his bachelor’s degree in the school of vehicle and mobility from Tsinghua University, in 2018. His research interests include traffic state estimation and traffic network optimization with connected and automated vehicles.
Traffic signal optimization with connected vehicle trajectories
Traffic signal retiming is one of the most cost-effective methods for reducing congestion and energy consumption in urban areas based on the existing road infrastructure. However, high installation and maintenance costs of vehicle detectors have prevented the widespread implementation of adaptive traffic control systems (ATSC). Therefore, most intersections are still controlled by fixed-time traffic signals which are not updated regularly due to the lack of traffic monitoring capabilities. In the past few years, vehicle trajectory data has become increasingly available and offers many advantages over detectors and other infrastructure-based sensors for traffic monitoring; but using such data for automatic traffic signal diagnosis and optimization at scalable implementable levels is relatively unexplored. To fill this gap, this work proposes Optimizing Traffic Signals as a Service (OSaaS), an integrated traffic signal re-timing system that uses vehicle trajectories as the main input. OSaaS addresses many of the current challenges relating to signal retiming with trajectory data such as incomplete observation due to limited penetration rates. The system builds a queueing model that reconstructs the overall average traffic state, calibrated from performance measurements directly obtained from vehicle trajectories. The calibrated queueing model then predicts and evaluates network performance under different traffic signal parameters to provide diagnostics and direct traffic signal retiming guidance. In April 2022, a citywide field test of OSaaS was conducted in Birmingham, Michigan, with 34 signalized intersections. This resulted in decreases in both the delay and number of stops by up to 20% and 30%, respectively. OSaaS provides a more scalable, sustainable, resilient, and efficient solution to traffic signal retiming without requiring any additional infrastructure through the exclusive utilization of currently available trajectory data. As a result, it presents the possibility of upgrading all existing fixed-time traffic signals to dynamic systems with periodical parameter updates, something that is not currently possible without significant investments in infrastructure-based traffic flow sensors.

This event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

PhD Seminar: Xintao Yan
2023-03-16 16:30 - 17:00 | Venue TBA, MI, 48109, United States
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speaker:
Xintao Yan, PhD Candidate, Civil and Environmental Engineering and Scientific Computing
Xintao Yan is currently a Ph.D. candidate in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor, advised by Professor Henry Liu. He received his bachelor’s degree from the Department of Automotive Engineering at Tsinghua University, China in 2018. His research interests are mainly about the safety of connected and automated vehicles, including naturalistic driving behavior modeling and automated driving system evaluation.
Simulating Naturalistic Driving Environment for Autonomous Vehicles
Simulation provides a controllable, efficient, and low-cost venue for both developing and testing autonomous vehicles (AV). But for simulation to be an effective tool, statistical realism of the simulated driving environment is a must. In this talk, we will introduce methods to simulate naturalistic driving environment for AV testing purposes.

This event is part of MICDE’s seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Paul Kent, PhD, Distinguished Research Scientist at Oak Ridge National Laboratory
2023-04-12 16:00 - 17:00 | 411 West Hall (1085 S. University), 1085 S. University Ave, Ann Arbor, MI, 48109, United States
Dr Kent`s research is focusing on predicting and explaining the properties of materials using computer simulation. Over the last two decades, advances in simulation techniques coupled with increasing computer power have led to several methods that are able to predict physical properties of real materials to a useful accuracy. Moreover, these methods use little or no experimental data, making them especially valuable for the study of new materials and devices. Dr. Kent specializes in the application and development of these so-called “first principles” methods.
His research interests are broadly focused on atomistic materials simulation. His ongoing research projects include:

Quantum Monte Carlo for real materials
Large length and timescale quantum molecular dynamics calculations
Characterization, optimization, and design of nanoscale systems with desired properties
Combined density functional and many-body calculations of correlated electron systems such as the copper-oxide superconductors
Reactive classical molecular dynamics
Simulation methods for exploitation of Exascale supercomputers and emergent architectures

Dr. Kent is the director of the Center for Predictive Simulation of Functional Materials. He also leads the development of the QMCPACK application for exascale computing as part of the Exascale Computing Project. QMCPACK is a high-performance Quantum Monte Carlo code for computing the electronic structure of atoms, molecules and solids, including metals. QMCPACK is open source and available on GitHub.
Dr Kent is a member of the Nanotheory Institute at the Center for Nanophase Materials Sciences (CNMS) and the Computational Chemical and Materials Science group in the Computational Science and Engineering Division. He spent three years at NREL with Alex Zunger after completing his PhD with Richard Needs at the University of Cambridge. For several years he worked with Mark Jarrell at the University of Cincinnati on high-temperature cuprate superconductors. In 2009 he transitioned from JICS/UT Knoxville to ORNL.
Awards:

ORNL Director’s Award for Outstanding Individual Accomplishment in Science and Technology, 2020.
APS Fellowship, nominated by the Division of Computational Physics, 2017.
ACM Gordon Bell Prize, 2008.

Professional Service:

Grant reviewer for US DOE and NSF
Reviewer for APS, ACS, IOP, Elsevier, Springer Nature etc.

Accurate Quantum Materials Predictions on the Largest Supercomputers
Advances in the field of computational materials science have helped to predict, understand, and optimize the properties of many classes of materials. These include new battery electrodes, catalysts, and arguably even higher-temperature superconductors. However, we still lack a widely usable method where all the key uncertainties and approximations in the predictions can be assessed and systematically reduced. This is critical where the approximations in established methods fail, such as in quantum materials, or simply where greater accuracy is desired. In this talk I will first describe our recent advances in Quantum Monte Carlo methods that promise to meet this challenge. Second, I will describe the new algorithms and performance portable software design and development strategies we have adopted to run efficiently on the largest supercomputers powered by GPU accelerators from NVIDIA, AMD and Intel. The lessons learned can be applied in any area of scientific software development.

The MICDE Winter 2023 Seminar Series is open to all. University of Michigan faculty and students interested in predicting and explaining the properties of materials using computer simulation are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Physics. Dr. Kent will be hosted by Dr. Emanuel Gull, Associate Professor of Theoretical Condensed Matter Physics.
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

MICDE / AIM Seminar: Lin Lin, Professor of Mathematics at University of California Berkeley
2023-09-22 15:00 - 16:00 | East Hall – 1084
Bio: Lin Lin is a Professor in the Department of Mathematics at UC Berkeley, and a Faculty Scientist in the Mathematics Group at Lawrence Berkeley National Laboratory. His research centers on solving quantum many-body problems by employing both classical and contemporary methods. These techniques prove valuable across various domains, including quantum chemistry, quantum physics, materials science, and quantum information theory. He has received the Sloan Research Fellowship (2015), the National Science Foundation CAREER award (2017), the Department of Energy Early Career award (2017), the (inaugural) SIAM Computational Science and Engineering (CSE) early career award (2017), the Presidential Early Career Awards for Scientists and Engineers (PECASE) (2019), the ACM Gordon Bell Prize (Team, 2020), and the Simons Investigator in Mathematics award (2021).
Quantum algorithms for eigenvalue problems
The problem of finding the smallest eigenvalue of a Hermitian matrix, known as the ground state energy in quantum physics, has broad applications. Recent years have witnessed significant algorithmic progresses including near-optimal asymptotic complexity, algorithms with a minimal number of required logical qubits, and even optimized preconstants. In this talk, I will first introduce basic quantum algorithm concepts for a non-expert audience and overview these advancements. I will then introduce a recent progress in leveraging ideas from open quantum systems to solve the eigenvalue problem, which allows us to start from a state with zero overlap with the target state.

The MICDE Fall 2023 Seminar Series is open to all. University of Michigan faculty and students interested in predicting and explaining the properties of materials using computer simulation are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and Applied & Interdisciplinary Mathematics (AIM). Prof. Lin will be hosted by Dr. Emanuel Gull, Associate Professor of Theoretical Condensed Matter Physics.
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

MICDE / ME Seminar: Erik Draeger, Director of the High Performance Computing Innovation Center and RADIUSS project at Lawrence Livermore National Laboratory
2023-10-27 16:00 - 17:00 | 1670 Bob and Betty Beyster Building, 2260 Hayward Street, Ann Arbor, MI, 48109, United States
Bio: Dr. Erik Draeger is the Director of the High Performance Computing Innovation Center and RADIUSS project at Lawrence Livermore National Laboratory as well as the Scientific Computing group leader at the Center for Applied Scientific Computing. He is also the Deputy Director of Application Development for the Exascale Computing Project, jointly overseeing a portfolio of 22 Office of Science applications, 4 NNSA applications, and 7 co-design projects. Erik earned a Bachelor’s degree in Physics from the University of California, Berkeley in 1995 and received a PhD in theoretical condensed matter physics from the University of Illinois, Urbana-Champaign in 2001. He has over a decade of experience developing scientific applications to achieve maximum scalability and time to solution on next-generation architectures. He has been a finalist for the Gordon Bell Prize six times since 2005 and won the prize in 2006.
Supercomputing at the exascale and beyond: future trends and challenges
For the past seven years, the U.S. Department of Energy’s Exascale Computing Project (ECP) has funded a comprehensive push to refactor 24 application projects to efficiently utilize exascale computing hardware to solve a varied set of complex science and engineering problems. Ambitious performance and capability goals were set for each application that demanded end-to-end rethinking of traditional approaches. Through detailed performance analysis, integration with optimized co-design frameworks and software libraries, and the use of programming abstractions to manage data placement and kernel execution, ECP applications recently demonstrated substantial capability and performance improvements on newly-available exascale machines. Despite significant diversity in the methods and algorithms underlying the ECP application portfolio, several common themes emerged in how to best adapt computational workloads to heterogeneous architectures. In this talk, an overview of best practices and lessons learned on effectively utilizing exascale hardware from the perspective of ECP applications will be presented. Strategies for developing portable, performant code will be discussed and examples of reexamining traditional algorithms and methods will be described. Armed with this knowledge, researchers can go beyond simply surviving an uncertain and turbulent computing future to instead leading a wave of scientific and computational innovation as traditional approaches are reexamined and new approaches adopted.

The MICDE Fall 2023 Seminar Series is open to all. University of Michigan faculty and students interested in predicting and explaining the properties of materials using computer simulation are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Mechanical Engineering (ME). Dr. Draeger will be hosted by Dr. Vikram Gavini, Professor of Mechanical Engineering.
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

MICDE / NERS Seminar: Larry Aagesen, Computational Scientist at Idaho National Laboratory
2023-11-01 15:00 - 16:00 | 2150 H.H. Dow, 2300 Hayward St, Ann Arbor, 48109, United States
Bio: Dr. Larry Aagesen is a Computational Scientist at Idaho National Laboratory (INL), and is the leader of the Computational Microstructure Science group there. He is a member of the development team for Marmot, INL’s application for simulating microstructural evolution in nuclear fuels and reactor structural materials, which is based on MOOSE, INL’s framework for solving partial differential equations using the finite element method. His primary area of expertise is in the phase-field method, having developed phase-field models for a variety of physical phenomena, including fission gas bubble evolution, solid-state precipitation, solidification and coarsening in metallic alloys and ceramics, and semiconductor growth. He received his undergraduate degree in Physics at the University of California, Berkeley in 1997, followed by service in the U. S. Navy’s nuclear propulsion program and work in industry. He then returned to graduate school, completing his Ph.D. in Materials Science and Engineering at Northwestern University in 2010. This was followed by appointment as a postdoctoral researcher and Assistant Research Scientist in the Department of Materials Science and Engineering at the University of Michigan from 2010 to 2015, after which he joined INL.
Multi-scale modeling of the evolution of structure and properties in materials for nuclear energy applications
Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. These modeling efforts make extensive of MOOSE (Multiphysics Object-Oriented Simulation Environment), a general-purpose open source finite element framework developed at INL. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

The MICDE Fall 2023 Seminar Series is open to all. University of Michigan faculty and students interested in predicting and explaining the properties of materials using computer simulation are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Nuclear Engineering and Radiological Sciences, (NERS). Dr. Aagesen will be hosted by Dr. Kevin Field, Associate Professor of Nuclear Engineering.
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.

Women in Computational Science Mini-Symposium (DISCOVER)
2023-11-27 14:00 - 17:00 | West Hall – 340
Women in Computational Science

The Women in Computational Science Symposium is the inaugural event for MICDE’s DISCOVER (Diversity and Innovation in Scientific Computing: Opportunities for Valuing Exploration and Representation) mini-symposium series. This mini-symposium provides a unique opportunity to delve into the pioneering research conducted by women in computational science while also gaining insight into their personal experiences and the challenges they face as researchers.
This year’s Women in Computational Science Symposium features:
Keynote speaker: Katrin Heitmann, Deputy Division Director Argonne National Laboratory – @ 3 pm
Bio: Katrin Heitmann is the deputy director of Argonne’s High Energy Physics division, and a physicist and computational scientist. She is also a Senior Associate for the Kavli Institute for Cosmological Physics at the University of Chicago and a member of NAISE at Northwestern. Before joining Argonne, Katrin was a staff member at Los Alamos National Laboratory. Her research currently focuses on computational cosmology, in particular on trying to understand the causes for the accelerated expansion of the Universe. She is responsible for large simulation campaigns with HACC and for the tools in the associated analysis library, CosmoTools. Katrin is a member of several major astrophysical surveys that aim to shed light on this question and is currently the Spokesperson for the LSST Dark Energy Science Collaboration.
Exploring the Dark Universe
Cosmology – the study of the origin, evolution, and constituents of the Universe – is now entering one of its most scientifically exciting phases. Three decades of surveying the sky have culminated in the celebrated “Cosmological Standard Model”. Yet, two of its key pillars, dark matter, and dark energy – together accounting for 95% of the mass-energy of the Universe – remain mysterious. Next-generation observatories will open new routes to understand the true nature of the “Dark Universe”. These observations will pose tremendous challenges on many fronts – from the sheer size of the data that will be collected to its modeling and interpretation. The interpretation of the data requires sophisticated simulations on the world’s largest supercomputers. The cost of these simulations, the uncertainties in our modeling abilities, and the fact that we have only one Universe that we can observe opposed to carrying out controlled experiments, all come together to create a major test for statistical methods of data analysis. In this talk, I will give a brief introduction to the Dark Universe and outline the challenges ahead. I will describe how complex, large-scale simulations will be used to extract the cosmological information from ongoing and next-generation surveys.
Guest speakers @ 2 pm
Liz Livingston, PhD candidate in Mechanical Engineering and Scientific Computing at U-M
Title: Data to Differential Equations – Discovering Mathematical Models for Biological Systems
Bio: Liz Livingston is a 5th year PhD candidate in Mechanical Engineering and Scientific Computing at the University of Michigan, advised by Professor Krishna Garikipati and Professor Alberto Figueroa. Her research focuses on data-driven modeling of biological systems. This work spans a range of topics including biomechanics, numerical methods, and high-performance computing. She received her BS and MS degrees from the University of Illinois at Urbana-Champaign where she studied the strength and microstructure of bone. Liz enjoys teaching and cultivates this interest through hands-on experience, outreach, and involvement in the American Society for Engineering Education (ASEE).
Abstract: Complex phenomena, such as those observed in biological systems, can typically be modeled with partial differential equations (PDEs). Finding governing equations can be a daunting task, often involving simplifications to the system such that the PDE does not fully capture the physics of the problem. Instead of reducing the complexity of the system with successive approximations, the governing PDE can be discovered using data. One of the fastest and most popular techniques is machine learning, where a surrogate is found as an approximation to the function. Alternatively, inference techniques may be used to identify the strong or weak form of the governing equation via parameter estimation. The tools we develop for the discovery of governing equations have applications in many complex systems, including biological ones such as flow through a stenosed artery and fracture in soft tissues. The goal of my PhD thesis is to develop and improve these mathematical methods to help expand our understanding of complex biological systems.

Rachel Niemer, Managing Director of WISE (Women in Science and Engineering)
Title: Who is WISE for and what should we do? Exploring levers of change to foster equity in STEM
WISE info: The University of Michigan is at the forefront of equality in science and engineering, and our focus on diversity, equity, and inclusion spans multiple dimensions, including gender, race, SES, first generation status, to name a few. The University of Michigan’s Women in Science and Engineering (WISE) unit aims to increase the participation by women and gender minorities in careers in science, technology, engineering and mathematics, and to foster their academic and professional success. We do this by cultivating students’ skills to thrive in STEM, strengthening the community working toward STEM equity, and working to mitigate systemic forces that impede retention of women, and individuals from other historically underrepresented groups, in STEM.
Abstract: As we look at the evolving landscape of where women, and other individuals from historically marginalized groups, thrive and persist in STEM, it makes sense to ask why more progress hasn’t been made. Women in Science and Engineering has been a resource for U-M students in STEM since 1980. Over that time, WISE, and similar units at other institutions, have experimented with a range of interventions to help women thrive in STEM. What if we chose the wrong levers for change? Are there radically new ways we might support efforts to graduate more STEM majors from minoritized communities? This presentation will explore different models for advancing STEM equity.
Panel discussion on navigating scientific careers – @ 4:10 pm

Katrin Heitmann, Deputy Division Director Argonne National Laboratory
Lisa Mesaros, Vice President, Product Management, Simulation and Test Solutions at Siemens Digital Industries Software
Liz Livingston, Clare Boothe Luce Fellow & PhD candidate in Mechanical Engineering and Scientific Computing, University of Michigan
Rachel Niemer, Managing Director of WISE (Women in Science and Engineering), University of Michigan

MICDE / Astronomy Seminar: Shy Genel, Associate Research Scientist at the Flatiron Institute, Simons Foundation
2024-01-16 14:00 - 15:00 | 411 West Hall (1085 S. University), 1085 S. University Ave, Ann Arbor, MI, 48109, United States
Bio: Dr. Shy Genel is an astrophysicist working in the field of computational galaxy formation and cosmology; he is studying how galaxies form and evolve and how they can be used to infer fundamental properties of our Universe. The main tool he employs in his research is cosmological hydrodynamical simulations, which run on supercomputers and generate digital ‘mini-universes’ that can be analyzed in ways that are not available with observational data. In recent years he has been employing machine learning models to develop novel ways to extract information from this type of simulations.
Dr. Genel received his PhD in 2011 under the guidance of 2020 Nobel Prize in Physics laureate Prof. Reinhard Genzel at the Max-Planck-Institute for Extraterrestrial Physics in Garching, near Munich. Between 2011-2016 he completed post-doctoral fellowships at the Harvard-Smithsonian Center for Astrophysics and at Columbia University, and in 2016 he joined the newly-founded Center for Computational Astrophysics at the Flatiron Institute (a division of the Simons Foundation), where he serves today as a Research Scientist.
Cosmological Hydrodynamical Simulations and Machine Learning at the Intersection of Galaxy Formation and Cosmology
As galaxy surveys encode a wealth of information about the basic properties of our Universe, improved modeling of galaxy formation will result in improved constraints on cosmology and fundamental physics. Cosmological hydrodynamical simulations, which follow the coupled evolution of dark and ‘normal’ matter from cosmologically motivated initial conditions, are a primary tool for studying how galaxies form. After a brief review of the revolution of the past decade in the scale and fidelity of cosmological simulations, I will discuss a new direction the field is taking in the past few years, where machine learning is opening new ways to extract cosmological information from the non-linear process of galaxy formation.

The MICDE Winter 2024 Seminar Series is open to all. University of Michigan faculty and students interested in predicting and explaining the properties of materials using computer simulation are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Astronomy, Dr. Genel will be hosted by Dr. Monica Valluri, Research Professor of Astronomy.
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.

MICDE / CEE Seminar: Michael D. Shields, Associate Professor of Civil & Systems Engineering at Johns Hopkins University
2024-01-26 12:00 - 13:00 | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States
Bio: Michael D. Shields is an Associate Professor in the Department of Civil & Systems Engineering at Johns Hopkins University. He holds a secondary appointment in the Department of Materials Science and Engineering, and is a fellow of the Hopkins Extreme Materials Institute. Prof. Shields conducts methodological research in uncertainty quantification (UQ) and probabilistic modeling for problems in mechanics, materials science, and physics with applications ranging from multi-scale material modeling to assessing the reliability and safety of large-scale structures. He received his Ph.D. in Civil Engineering and Engineering Mechanics from Columbia University in 2010, after which he was employed as a Research Engineer in applied computational mechanics at Weidlinger Associates, Inc. He joined the faculty at Johns Hopkins in 2013. For his work in UQ, Prof. Shields has been awarded the ONR Young Investigator Award, the NSF CAREER Award, the DOE Early Career Award, and the Johns Hopkins University Catalyst Award. Prof. Shields and his group also develop the open-source UQpy (Uncertainty Quantification with Python) software, which is a general toolbox and development environment for UQ in computational, mathematical, and physical systems.
UQ for ML and ML for UQ: Why Uncertainty Quantification and Machine Learning Go Hand-in-Hand
Uncertainty Quantification (UQ) and Machine Learning (ML) play an increasingly important role in physics-based computational modeling. Especially with the recent rise of scientific machine learning (SciML) and physics-informed ML, new computational tools are being harnessed to solve bigger and more challenging problems. Moreover, UQ has become an integral part of any physics-based modeling effort because our models, as carefully developed as they may be, are rife with uncertainties (both epistemic and aleatory) in their parameters, inputs/excitations, and sometimes in the form of the models themselves. When SciML methods are then applied in these applications, additional uncertainties are introduced. In this talk, I will broadly introduce the interrelated roles that UQ and ML play in physics-based modeling. I specifically distinguish between “UQ for ML” and “ML for UQ” and describe the important role that each plays in the modern physics-based computational modeling paradigm – demonstrating the role of UQ/ML in various applications of interest ranging from multi-scale materials modeling to high energy-density physics.

The MICDE Winter 2024 Seminar Series is open to all. University of Michigan faculty and students interested in predicting and explaining the properties of materials using computer simulation are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Civil and Environmental Engineering (CEE). Dr. Shields will be hosted by Dr. Evgueni Filipov, Associate Professor of Civil and Environmental Engineering.
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

Data Science/Computational Social Science/MICDE Seminar: Yian Ma, Assistant Professor, UC San Diego
2024-09-18 12:00 - 13:00 | 438 West Hall, 1085 UNIVERSITY AVE, Ann Arbor, 48109, United States
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.
MCMC, variational inference, and reverse diffusion Monte Carlo
Prof. 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.
RSVP HERE

The MICDE Winter 2025 Seminar Series is open to all. University of Michigan faculty and students.
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

MICDE/ME Seminar: Krishnan Mahesh, Professor, University of Michigan NAME
2024-10-01 15:00 - 16:00 | 2150 H.H. Dow, 2300 Hayward St, Ann Arbor, 48109, United States
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.
Large Eddy Simulation of Turbulent Cavitating Flows
Cavitation 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.
LES 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.

The MICDE Fall 2025 Seminar Series is open to all. University of Michigan faculty and students.
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

MICDE Nobel Prize Lectures
2025-09-24 15:00 - 17:00 | Forum Hall, Palmer Commons, 100 Washtenaw Ave, Ann Arbor, MI, 48109, United States
Speakers:

Charles 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.
Veera Sundararaghavan, Professor of Aerospace Engineering and the director of Multiscale Structural Simulations Laboratory, will talk about the 2024 Nobel Prizes in Physics.

Nobel Prize Lectures
The 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.
Panel Discussion:
The 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?
Panelists:

James Wells, Professor of Physics, University of Michigan
Indika Rajapakse, Professor of Computational Medicine and Bioinformatics, and Professor of Mathematics, University of Michigan
Charles Brooks, Warner-Lambert/Parke-Davis Professor of Chemistry, Cyrus Levinthal Distinguished University Professor of Chemistry and Biophysics
Veera Sundararaghavan, Professor of Aerospace Engineering and the director of Multiscale Structural Simulations Laboratory

Moderator:

Karthik 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

MICDE - Mechanical Engineering Seminar - Elif Ertekin, University of Illinois Urbana-Champaign
2026-01-29 14:00 - 15:00 | Lurie Robert H. Engin. Ctr – Johnson Rooms (LEC 3213)
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
Physical Review Letters.
Physical Mechanisms or Learned Patterns? Reconciling First-Principles Models with Machine Learning for Predictive Materials
Predictive 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.

The MICDE 2025-26 Seminar Series is open to all.
This 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.
This is an in-person event. This seminar will not be recorded!
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

MICDE - NERS - MIPSE Joint Seminar: Brian Haines, Los Alamos National Laboratory
2026-02-10 15:00 - 16:00 | Lurie Robert H. Engin. Ctr – Johnson Rooms (LEC 3213)
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.

Radiation-hydrodynamics Modeling & Application to Prediction of Inertial Confinement Fusion Experiments
The 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.

The MICDE 2025-26 Seminar Series is open to all.
This 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).
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

Mathematics - MICDE - MCAIM joint colloquium: Peter Bosler, Sandia National Laboratories
2026-03-17 16:00 - 17:00 | 1360 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
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.
Accelerating Earth System Simulation
Abstract: 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.

The MICDE 2025-26 Seminar Series is open to all.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

MICDE - Mechanical Engineering seminar: Phani Motamarri, Indian Institute of Science, Bangalore
2026-05-29 11:00 - 12:00 | 1311 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
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.
His 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.
Prof. 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.
Inexact yet Accurate: Unlocking Quantum Modeling of Materials at Scale through Approximation-Tolerant Algorithms
Abstract: 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.

The MICDE 2025-26 Seminar Series is open to all.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu
