MICDE / AIM: Youngsoo Choi, Research Scientist, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory
2021-12-03 15:00 - 16:00 (America/Detroit) | Zoom Event, MI, United States
Zoom link | Meeting ID: 964 5038 3843 | Psswd: 010182

Bio: Youngsoo is a computational math scientist in CASC under Computing directorate at LLNL. He is currently leading data-driven reduced order model development team for various physical simulations, with whom he developed the open source codes, libROM (https://www.librom.net) and LaghosROM (https://github.com/CEED/Laghos/tree/rom/rom). libROM is a library for reduced order models and LaghosROM implements reduced order models for Lagrangian hydrodynamics (https://authors.elsevier.com/c/1e3CuAQEIviQh). He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University with applied mathematics as minor and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc in Sandia National Laboratory and Stanford University prior to joining LLNL in 2017.
Physics-constrained data-driven methods for accurately accelerating simulations
A data-driven model can be built to accurately accelerate computationally expensive physical simulations, which is essential in multi-query problems, such as inverse problem, uncertainty quantification, design optimization, and optimal control. In this talk, two types of data-driven model order reduction techniques will be discussed, i.e., the black-box approach that incorporates only data and the physics-constrained approach that incorporates the first principle as well as data. The advantages and disadvantages of each method will be discussed. Several recent developments of generalizable and robust data-driven physics-constrained reduced order models will be demonstrated for various physical simulations as well. For example, a hyper-reduced time-windowing reduced order model overcomes the difficulty of advection-dominated shock propagation phenomenon, achieving a speed-up of O(20~100) with a relative error much less than 1% for Lagrangian hydrodynamics problems, such as 3D Sedov blast problem, 3D triple point problem, 3D Taylor–Green vortex problem, 2D Gresho vortex problem, and 2D Rayleigh–Taylor instability problem. The nonlinear manifold reduced order model also overcomes the challenges posed by the problems with Kolmogorov’s width decaying slowly by representing the solution field with a compact neural network decoder, i.e., nonlinear manifold. The space–time reduced order model accelerates a large-scale particle Boltzmann transport simulation by a factor of 2,700 with a relative error less than 1%. Furthermore, successful application of these reduced order models for mate-material lattice–structure design optimization problems will be presented. Finally, the library for reduced order models, i.e., libROM (https://www.librom.net), and its webpage and several YouTube tutorial videos will be introduced, which is useful for education as well as research purpose.

The MICDE Fall 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Applied and Interdisciplinary Mathematics program at the University of Michigan. Dr. Choi will be hosted by Dr. Jesse Capecelatro, Assistant Professor of Mechanical Engineering and Aerospace Engineering.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Sarah Hormozi, Associate Professor, Cornell University
2021-12-08 15:00 - 16:00 | Zoom Event, MI, United States
WATCH THE RECORDING HERE.

Bio: Sarah Hormozi is an associate professor of Chemical and Biomolecular Engineering at Cornell University. Her expertise lies in complex fluid mechanics, rheology, and soft matter physics. Her research has been recognized by a number of awards, including the National Science Foundation CAREER award and the ACS Petroleum Research Fund Doctoral New Investigator Award. She also serves on the advisory boards of Journals of Physical Review Fluids, Non-Newtonian Fluid Mechanics, The American Institute of Chemical Engineers, and Physics of Fluids.
Slurries of complex fluids
Suspensions of non-Brownian particles in viscous fluids, for which thermal fluctuations are negligible, are relevant in industrial processes (e.g. waste disposal, concrete, drilling muds, metalworking chip transport, and food processing) and in natural phenomena (e.g. flows of slurries, debris, and lava). It is also relevant to mention that some biological and smart materials can be designed from various suspensions, drawing attention to applications in physiology, bio
locomotion, shock absorbers, and beyond. This countless number of suspensions has a wide range of nonlinear rheological behaviors, such as shear thinning, shear thickening, shear banding, yield stress, and finite normal stress differences even when inertia is negligible.
For applications enumerated above, even small increases in efficiency when processing slurries of complex fluids could make significant positive economic and environmental impacts. Obviously, a thorough understanding of the rheology and fluid mechanics of these materials in natural and industrial settings is essential to improving the efficiency of production. However, this is extremely challenging due to the complex rheology of the suspending fluids, the interaction of fluid and particle phases, and multiple-body and short-range interactions of particles. My presentation will introduce an array of experimental and modeling techniques that my research team uses to investigate the rheological properties and fluid dynamical behavior of complex suspensions. The goal is to establish a continuum framework and refine it through a series of microstructure investigations. I will discuss how our recent results can be used to address and resolve some of the industrial issues. Finally, open questions will be disclosed, which must be answered to build a firm foundation for a long-term contribution to the area of complex suspensions.

The MICDE Fall 2021 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This seminar is hosted by the Michigan Institute for Computational Discovery and Engineering (MICDE) at the University of Michigan. Dr. Hormozi will be hosted by Dr. Mariana Carrasco-Teja, MICDE Associate Director and Assistant Research Scientist. Questions? Email MICDE-events@umich.edu

James Stokes, Research Fellow, Flatiron Institute
2021-12-08 16:00 - 17:00 | B844 East Hall, 530 Church St, Ann Arbor, MI, 48109, United States
In person event (no zoom available)!!

Bio: Dr. James Stokes is a Research Fellow at the Flatiron Institute with a joint position at the Computational Center for Quantum Physics and the Center for Computational Mathematics. He completed his Ph.D at the University of Pennsylvania, focusing on quantum field theory. His recent research intersects machine learning, quantum information and condensed matter physics. Previously, James was a postdoctoral researcher in the Computer and Information Science Department at University of Pennsylvania from 2017 to 2018. James also worked as a quant in the finance industry before his postdoc and as a research scientist at a machine learning startup.

Geometry and numerics of variational quantum algorithms and classical counterparts
Stokes will review a family of variational algorithms which have been proposed as candidates to make use of near- to intermediate-term quantum computers, placing particular emphasis on geometric and numeric features that are shared by classical variational stochastic approximation algorithms. Stokes will also discuss some applications of this hybrid quantum-classical approach to scientific and engineering problems beyond its traditional domain of application.

This seminar is co-hosted by the Michigan Center for Applied & Interdisciplinary Mathematics (MCAIM) and the Michigan Institute for Computational Discovery and Engineering (MICDE) at the University of Michigan. Dr. Stokes will be hosted by Prof. Shravan Veerapaneni, professor of mathematics. Questions? Email MICDE-events@umich.edu

MICDE / AIM Seminar: Blaise Bourdin, PhD, Professor of Mathematics & Statistics, McMaster University
2022-02-25 15:00 - 16:00 | Zoom Event
Zoom Link
Bio: Blaise Bourdin is a professor in the department of mathematics and statistics at McMaster University (Hamilton, ON, Canada). Dr. Bourdin’s formal training is at the meeting point of solid mechanics, scientific computing, and applied mathematics. He borrows techniques for these areas to study problems in mechanical science with a specific focus on problems involving defect mechanics and optimal design. He has cultivated this multi-disciplinary training by revisiting “classical” problems with more advanced technical tools, by skewing my theoretical work towards problems of particular relevance to engineering and science, and by using investigative numerical simulations as a modeling tool in complex multi-scale problems.
Dr. Bourdin’s research focusses on modeling, analysis, and numerical implementations of problems arising in reservoir engineering, defect mechanics, optimal design, and image processing. He is the recipient of multiple research grants from the National Science Foundation, the Louisiana Board of Regents and industry, totalling over $6M. He has written several high impact publications, including three ESI highly cited papers in two disciplines (mathematics and engineering). He also maintains several open source software projects.
VARIATIONAL AND PHASE-FIELD MODELS OF BRITTLE FRACTURE: PAST SUCCESSES AND CURRENT ISSUES
In this talk Dr. Bourdin will start with a modern interpretation of Griffith’s classical criterion as a variational principle for a free discontinuity energy and will recall some of the milestones in its analysis. Then he will introduce the phase-field approximation per se and describe its numerical implementation. He will illustrate how phase-field models have led to major breakthroughs in the predictive simulation of fracture in complex situations. He will show how this applies to current issues, including crack nucleation in nominally brittle materials, fracture of heterogeneous materials, and inverse problems.

The MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Applied & Interdisciplinary Mathematics program at the University of Michigan. Dr. Bourdin will be hosted by Dr. Selim Esedoglu, Professor of Mathematics.
This is a hybrid event and will be held in-person and broadcasted online via Zoom. Join here.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Marta D`Elia, Principal Member of the Technical Staff, Sandia National Laboratories
2022-03-14 14:00 - 15:00 | Zoom Event, MI, United States
WATCH THE RECORDING HERE.
Bio: Marta D’Elia is a Principal Member of the Technical Staff at Sandia National Laboratories, where she works since 2014. She’s currently part of the Data Science and Computing group at the California site. She obtained her master degree in Mathematical Engineering at Politecnico of Milano with Prof. Quarteroni and she obtained her Ph.D in Applied Mathematics at Emory University with Prof. Veneziani. There, she worked on optimal control in CFD for cardiovascular applications. She was a postdoctoral fellow at Florida State University where she worked with Prof. Gunzburger on optimization and control for nonlocal and fractional models. She’s an associate editor of the SIAM Journal on Scientific Computing, Advances in Continuous and Discrete Models, Numerical Methods for PDEs, and the Journal of Peridynamics and Nonlocal Models. Also, she’s a co-founder of the One Nonlocal World project. Her interests include nonlocal modeling and simulation, optimization and optimal control, and scientific machine learning.
Scientific interests:

Modeling and Computational aspects of Nonlocal and Fractional equations,
Scientific Machine Learning,
Optimization and Uncertainty Quantification.

DATA-DRIVEN LEARNING OF NONLOCAL MODELS: BRIDGING SCALES WITH NONLOCALITY
Nonlocal models are characterized by integral operators that embed length scales in their definition. As such, they are preferable to classical partial differential equation models in situations where the dynamics of a system is affected by the small scale behavior, yet the small scales would require prohibitive computational cost to be treated explicitly. In this sense, nonlocal models can be considered as coarse-grained, homogenized models that, without resolving the small scales, are still able to accurately capture the system’s global behavior. However, nonlocal models depend on “kernel functions” that are often hand tuned.
We propose to learn optimal kernel functions from high fidelity data by combining machine learning algorithms, known physics, and nonlocal theory. This combination guarantees that the resulting model is mathematically well-posed and physically consistent. Furthermore, by learning the operator rather than a surrogate for the solution, these models generalize well to settings that are different from the ones used during training. We apply this learning technique to find homogenized nonlocal models for subsurface solute transport solely on the basis of breakthrough curves.
We also apply the same kernel-learning technique to design new stable and resolution-independent deep neural networks, referred to as Nonlocal Kernel Networks (NKN). Stability of NKNs is obtained by imposing constraints derived from the nonlocal vector calculus, whereas deep training is performed by means of a shallow-to-deep initialization technique. We demonstrate the accuracy and stability of NKNs on PDE-learning and image-classification problems.

The MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational modeling and machine learning are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Department of Mechanical Engineering. Dr. D`Elia will be hosted by Dr. Krishna Garikipati, Professor of Mechanical Engineering, and of Mathematics.
This is a virtual event and will be broadcasted online via Zoom. MICDE students and fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu

MICDE / MIDAS Seminar: Yun S. Song, PhD, Professor of Computer Science and Statistics, University of California, Berkeley
2022-03-21 16:00 - 17:00 | West Hall 340
ZOOM LINK
Bio: Professor Yun S. Song is a professor of EECS and Statistics working in mathematical and computational biology. He received his BS degrees in mathematics and physics from MIT, and a PhD in physics from Stanford University. Prof. Song’s research centers around computational and mathematical biology. He is generally interested in developing computational tools and statistical methods to facilitate the research of the broad biomedical community, while also getting deeply involved in data analysis and interpretation. Prof. Song is also interested in machine learning, combinatorial optimization, algorithms, and Monte Carlo methods.
Recent honors and awards include NIH Pathway to Independence Award K99/R00 (2006), Alfred P. Sloan Research Fellowship (2008), Packard Fellowship for Science and Engineering (2008), NSF CAREER Award (2009), Jim and Donna Gray Faculty Award for Excellence in Undergraduate Teaching (2013), Miller Research Professorship (2014), Math+X Simons Chair (2015), and Chan Zuckerberg Biohub Investigator Award (2017).

Talk Title: Mathematical and machine learning models for predicting protein synthesis and function

Abstract: Proteins are the workhorses of the cell and are involved in all aspects of cellular processes. In spite of notable technological advances in protein biology and genomics over the past decade, it remains an important challenge to unravel how protein synthesis and function are affected by genetic mutations. In this talk, I will describe our recent progress in tackling this challenge by leveraging new theoretical results on interacting particle systems and recent advances in natural language processing.

The MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Michigan Institute for Data Science (MIDAS). Dr. Song will be hosted by Dr. George Zhang, Professor of Ecology and Evolutionary Biology.
This is a hybrid event and will be held in-person and broadcast online via Zoom. Note: You may register after the event has started.
Questions? Email MICDE-events@umich.edu

MICDE/AIM Seminar: Miguel Moyers-González, Associate Professor of Mathematics and Statistics, University of Canterbury
2022-04-01 15:00 - 16:00 | Your Desktop
WATCH THE RECORDING HERE.
Bio: Dr. Miguel Moyers-González completed his B.Sc. at the Instituto Tecnológico Autónomo de México and his M.Sc and Ph.D. in Mathematics at the University of British Columbia. He was a postdoctoral fellow at the Université de Montréal before joining the University of Durham as a Lecturer in Applied Mathematics. He is presently an Associate Professor in the School of Mathematics & Statistics at the University of Canterbury. Dr. Moyers-Gonzalez primary research interests are in the mathematical analysis and computation of complex fluid flows. In broad terms, the problems he has studied involve the combination of physical understanding, i.e. of a particular application, coupled with both theoretical and computational techniques for partial differential equations.
Inferring physical properties and topographical features from free surface flow data
The accurate modelling of geophysical flows often requires information that is difficult to measure and therefore poorly quantified. Such information may relate to the fluid properties or an unknown boundary condition, for example. The premise of this talk is that when the flow is bounded by a free surface, the deformation of this free surface contains useful information which can be used to infer such unknown quantities. The increasing availability of free surface data through remote sensing using drones and satellites provides the impetus to use mathematical methods and numerical tools to interpret the signature embedded in the free surface deformation.
In this talk, we will explore the problem of recovering simultaneously the ice thickness and basal slip of an ice flow governed by the shallow ice approximation.

The MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational sciences are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery (MICDE) and the Applied & Interdisciplinary Mathematics program at the University of Michigan. Dr. Moyers-González will be hosted by Dr. Mariana Carrasco-Teja, Assistant Research Scientist and Associate Director of MICDE.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
This is a virtual event.
Questions? Email MICDE-events@umich.edu

MICDE Seminar: Douglas Spearot, Professor of Mechanical & Aerospace Engineering, University of Florida
2022-04-05 15:00 - 16:00 | 1311 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
WATCH THE RECORDING HERE.
Bio: Dr. Douglas Spearot is a Newton C. Ebaugh Professor in the Department of Mechanical & Aerospace Engineering in the Herbert Wertheim College of Engineering at the University of Florida. He also holds an affiliate appointment in the Department of Materials Science & Engineering. From 2005-2015, he was an Assistant/Associate Professor in the Department of Mechanical Engineering and a member of the Institute for Nanoscience and Engineering at the University of Arkansas. His research focuses on the use of atomistic and mesoscale simulation techniques to study the mechanical and thermodynamic properties of materials, with particular focus on the behavior of dislocations and interfaces, and the development of computational tools to extract experimentally relevant metrics from simulation generated data. Dr. Spearot received his B.S. in Mechanical Engineering from the University of Michigan, and his M.S. and Ph.D. in Mechanical Engineering from the Georgia Institute of Technology.
Awards:

2010 NSF CAREER Award to elucidate the nanoscale mechanisms associated with phase selection during vapor deposition.
2007 Ralph E. Power Junior Faculty Enhancement Award to study plasticity in nanostructured materials.
2020 Teacher of the Year in the Department of Mechanical & Aerospace Engineering, University of Florida.
2014 College of Engineering Imhoff Outstanding Teaching Award, University of Arkansas.
2014 Arkansas Alumni Association Rising Teaching Award, University of Arkansas.

Mesoscale Modeling of Plasticity in Metallic Materials via Advancement of the Discrete Dislocation Dynamics Simulation Method
Plastic deformation in metallic materials is governed by the individual and collective behaviors of defects, such as dislocations and grain boundaries (GBs). Among computational methods for modeling this inherently multi scale problem, discrete dislocation dynamics (DDD) is a powerful mesoscale technique that explicitly simulates the dynamics and interactions of dislocations and provides a continuum-level understanding of plasticity. Yet, the utility of DDD simulations for certain problems is compromised by missing defect physics and limited linkages to experiments. The focus of this seminar will be on two advancements to the DDD method. First, a disclination-dislocation framework for modeling the mechanical structure of equilibrium GBs (EGBs) and nonequilibrium GBs (NEGBs) is incorporated into the DDD method. This approach accounts for the mechanical and kinetic effects of multiple transmission events, and the absorption of residual dislocations at the GB. DDD simulations reveal that accumulated dislocation content from prior slip transmission lowers the external driving stresses required for subsequent slip transmission, indicating GB softening. Second, to enhance the connection between DDD simulations and experiments, a new “virtual” diffraction method is developed to generate strain-broadened diffraction profiles from DDD microstructures. This method is used to generate a database of diffraction profiles from simulated dislocation microstructures, which enables a new data-driven approach for dislocation density prediction from diffraction line profile analysis.

The MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in the mechanical and thermodynamic properties of materials are encouraged to attend.
Dr. Spearot will be hosted by Dr. Yue Fan, Assistant Professor of Mechanical Engineering.
This is a hybrid event and will be held in-person and broadcast online via Zoom.
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 Seminar: Katya Scheinberg,Professor of Operations Research and Information Engineering, Cornell University
2022-04-14 10:30 - 11:30 | 1500 EECS
WATCH THE RECORDING HERE.
Bio: Dr. Katya Scheinberg is a Professor and Director of Graduate Studies at the School of Operations Research and Information Engineering at Cornell University. Prior to joining Cornell she was the Harvey E. Wagner Endowed Chair Professor at the Industrial and Systems Engineering Department at Lehigh University. She attended Moscow University for her undergraduate studies and received her PhD degree from Columbia University. She worked at the IBM T.J. Watson Research Center as a research staff member for over a decade before joining Lehigh in 2010.
Prof. Scheinberg’s main research areas are related to developing practical algorithms (and their theoretical analysis) for various problems in continuous optimization, such as convex optimization, derivative free optimization, machine learning, quadratic programming, etc. She is a recipient of the Lagrange Prize from SIAM and MOS, the Farkas Prize from Informs Optimization Society and the Outstanding Simulation Publication award from Informs Simulation Society.
Prof. Scheinberg is currently the editor-in-chief of Mathematics of Operations Research, and a co-editor of Mathematical Programming.

Overview of Adaptive Optimization Methods for Stochastic Oracles
Continuous optimization is a mature field, which has recently undergone major expansion and change. One of the key new directions is the development of methods that do not require exact information about the objective function. Nevertheless, the majority of these methods, from stochastic gradient descent to “zero-th order” methods use some kind of approximate first order information. We will introduce a general definition of a stochastic and show how this definition applies in a variety of familiar settings, including simple stochastic gradient via sampling, traditional and randomized finite difference methods and more. We will overview several stochastic methods and how the general definition extends to the oracles used by these methods.

The MICDE Winter 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery and Engineering (MICDE) and the Department of Industrial and Operations Engineering. Dr. Scheinberg will be hosted by Dr. Albert Berahas, Assistant Professor of Industrial and Operations Engineering.
This is a hybrid event and will be held in-person and broadcasted online via Zoom.
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 / IOE Seminar: Andreas Wächter, Professor of Industrial Engineering and Management Sciences, Northwestern University
2022-09-15 15:00 - 16:00 | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States
WATCH THE RECORDING HERE.
Andreas Wächter is a Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. His research interests include the design, analysis, implementation and application of numerical algorithms for nonlinear continuous and mixed-integer optimization. He obtained his master’s degree in Mathematics at the University of Cologne, Germany, in 1997, and this Ph.D. in Chemical Engineering at Carnegie Mellon University in 2002. Before joining Northwestern University in 2011, he was a Research Staff Member in the Department of Mathematical Sciences at IBM Research in Yorktown Heights, NY. He is a recipient of the 2011 Wilkinson Prize for Numerical Software and the 2009 Informs Computing Society Prize for his work on the open-source optimization package Ipopt.

THE ARPA-E GRID OPTIMIZATION COMPETITION
In recent years, the US Advanced Research Projects Agency-Energy (ARPA-E) has been organizing the “Grid Optimization Competition.” To participate, teams from academia and industry submitted computer program implementations of specialized algorithms for solving large realistic Security-Constrained Optimal Power Flow (SCOPF) problems. The performance of the solvers was tested and ranked independently by the organizers, using large-scale real-life instances. The goal of SCOPF is the determination of the most cost-efficient operation of an electrical power grid in a such way that it can withstand contingencies in the form of outages of any its components. Mathematically, this is an extremely large-scale two-stage nonlinear and nonconvex optimization problem. In this presentation, the approach of several teams will be described, including that of our own GO-SNIP team that placed second in the first challenge.
Following the seminar IOE is holding a small reception in IOE Commons – 1709, snacks and refreshments will be served.

The MICDE Fall 2022 Seminar Series is open to all. University of Michigan faculty and students interested in power grid optimization are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery & Engineering (MICDE) and the Department of Industrial and Operations Engineering. Prof. Wächter will be hosted by Dr. Salar Fattahi, Assistant Professor of Industrial and Operations Engineering and Dr. Siqian Shen, Associate Professor of Industrial and Operations Engineering and Associate Professor of Civil and Environmental Engineering.
This event is in-person only!
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 Seminar: Pania Newell, Assistant Professor of Mechanical Engineering, University of Utah
2022-09-23 16:00 - 17:00 | 1200 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
Pania Newell is currently an Assistant Professor in the Department of Mechanical Engineering and holds adjunct faculty positions at the School of Computing and Civil Engineering Department at the University of Utah. Before joining The University of Utah, she was a member of the technical staff at Sandia National Laboratories. She obtained her M.S. and Ph.D. from the University of New Mexico and the University of Colorado-Boulder, respectively. Her research interest lies at the interface of mechanics and material sciences. In particular, she is interested in multi-scale, multi-physics phenomena in heterogeneous porous materials through developing theoretical, computational, and experimental frameworks combined with data sciences. She is the co-founder/co-host of an academic podcast called “This Academic Life”.
MECHANICS OF HIERARCHICAL POROUS MATERIALS: DESIGN, CONTROL AND PREDICTION
Hierarchical porous materials with pores at multiple length scales are widespread in nature. Although different compositions, textures, and physical properties of natural porous materials have inspired researchers and engineers to design materials with controllable pore structures, the hierarchical structure of natural porous materials poses challenges in understanding damage and fracture in these complex systems. To be able to create nature-inspired materials, we must have a mechanistic understanding of materials ranging from the macro- to the nanoscale. In this talk, I will begin by providing an overview of porous materials and their substantial role in our energy sector. I will then discuss some of our recent efforts in designing nano/micro porous structures with different pore morphology and novel in-situ testing to highlight the effect of different structural and geometrical parameters in porous materials across scales. I will also show how computational tools enable us to enhance our fundamental understanding of fracture propagation mechanisms in such materials over a wide range of scales. At the nanoscale, molecular dynamics simulation provides information about mechanical properties, such as fracture energy release rate for various pore morphologies. At the micro-scale, the impact of the pore shape and size on fracture pattern is investigated through a two-scale homogenization method coupled with the state-of-the-art phase-field fracture technique. The results of this hierarchical coupling approach highlight the importance of higher-order parameters associated with the pore shape and size on fracture properties and patterns at the continuum scale.

The MICDE Fall 2022 Seminar Series is open to all. University of Michigan faculty and students interested in multi-scale, multi-physics phenomena in heterogeneous porous materials are encouraged to attend.
This seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Newell will be hosted by Prof. Krishna Garikipati, Professor of Mechanical Engineering and Mathematics and Director of MICDE.
This is an in-person event, Zoom link will only be provided upon request.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu
WATCH THE RECORDING HERE.

MICDE Seminar: John Tramm, Assistant Computational Scientist, Argonne National Laboratory
2022-10-27 15:30 - 16:30 | 1010 H. H. Dow, 2300 Hayward St, Ann Arbor, MI, 48109, United States
Dr. John Tramm is an assistant scientist in the computational science division at Argonne National Laboratory. He received his PhD in computational nuclear engineering from MIT in 2018. John’s research efforts are focused on improving neutron transport methods that drive massively parallel simulations of nuclear reactors using some of the world’s largest supercomputers. John also has deep experience developing and optimizing simulation applications for GPU-based systems.

THE RISE OF PORTABLE GPU PROGRAMMING: EXPERIENCES DEVELOPING GPU-BASED SCIENTIFIC SIMULATION APPLICATIONS FOR INTEL, NVIDIA, AND AMD GPUs
Historically, portability has not been important for GPU programming as NVIDIA has dominated the high performance computing (HPC) GPU market. With only one major GPU vendor available to choose from, it has always made sense to develop scientific HPC apps using NVIDIA’s proprietary CUDA programming model. However, in 2022 both AMD and Intel are releasing HPC GPU products with the intention of competing directly with NVIDIA. In fact, the world’s first exascale supercomputer (Oak Ridge National Laboratory’s Frontier) is powered by AMD GPUs, with another even larger exascale supercomputer (Aurora) powered by Intel GPUs set to arrive at Argonne National Laboratory shortly. These new computers highlight a trend not just from CPU to GPU in HPC, but also a trend from proprietary CUDA into a number of different portable performance models for GPU. Thus, scientific application developers are now confronted with not only the difficultly of porting or developing apps for GPU architectures, but also with selecting from a wide variety of portable GPU programming models (for instance, OpenMP offloading, HIP, SYCL/DPC++, OpenCL, Kokkos, RAJA, and OCCA).
In this talk, I will briefly introduce the newest supercomputing systems and will give an overview of the many different portable performance models now available for GPUs. I will show a few snippets of an example kernel implemented in a variety of different models, and will even compare performance of a scientific mini-app, XSBench, across all major programming models and GPU architectures. Subjective “pros and cons” of each programming model will be discussed along with quantitative performance comparisons. Next, I will use a full scientific GPU application (the OpenMC Monte Carlo particle transport code) as a case study to discuss real-world issues affecting portable scientific GPU applications and how bleeding-edge GPU compiler technology stacks are faring. I will also briefly discuss a few of the algorithmic performance optimizations that we developed for OpenMC to give a feel for what types of changes are required to achieve high performance on GPU.

The MICDE Fall 2022 Seminar Series is open to all.
This seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Dr. Tramm will be hosted by Prof. Brendan Kochunas, Assistant Professor of Nuclear Engineering and Radiological Sciences.
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

MICDE Seminar: Reese Jones, Distinguished Member of the Technical Staff, Sandia National Laboratories
2022-10-31 15:30 - 16:30 | 1303 EECS, 1301 Beal Ave, Ann Arbor, MI, 48109, United States
Reese Jones is currently a staff scientist at Sandia National Laboratories in Livermore, CA. He is engaged in materials science and computational physics research with scales ranging from atomic/molecular to the continuum. He has made contributions to multi-scale methods, electrochemical and thermal transport, atomic-level fracture, and contact. Recently he has been developing and applying machine learning methods to provide constitutive models for
complex materials, quantify material uncertainty, and interpret materials imaging for reliability analysis.
PREDICTING FAILURE IN POROUS METALS USING CONVOLUTIONAL NEURAL NETWORKS
Predicting whether defects are critical or not is a high-value task in medicine, materials engineering, and other fields. Tools that augment expert opinion are needed in the current era of high resolution imaging that can reveal an overwhelming number of defects. In particular, porosity is a persistent feature of additively manufactured materials and determines failure locations through complex mechanics that exhibit sensitivity to the initial pore locations. In the case of materials engineering expensive direct numerical simulations are available and can be used to train efficient surrogates. Neural networks, such as the one we have developed, enable more complete analysis of potential outcomes.
In this work, we develop convolutional neural networks as surrogate models for predicting failure
locations. The binary classification problem of categorizing intact/failed voxels is first regularized by recasting it as a regression problem for the continuous damage field subjected to pre-processing transformations. An apparent challenge is the damage fields display a relatively small number of voxels close to failure leading to a form of class imbalance for regression that can cause the optimizer to converge to a poor local minimum. We address this through a re-weighting of the loss function which accounts for the relative frequencies of damage values. Another challenging aspect is the high sensitivity of the outcomes to the porosity field which typically creates multiple regions of high damage competing for failure. This motivates the use of Bayesian neural networks to capture sensitivities in the prediction through uncertainty quantification. We use these uncertainties to rank the likelihood of failure of any particular cluster of porosity in a reliability analysis. Lastly, to aid transferability of the network and reduce the training burden when it is applied to new materials and processes, we are exploring transfer learning techniques.

The MICDE Fall 2022 Seminar Series is open to all.
This seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Dr. Jones will be hosted by Prof. Krishna Garikipati, Professor of Mechanical Engineering and Mathematics and Director of MICDE.
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

MICDE / AIM Seminar: Jennifer Franck, Assistant Professor of Engineering Physics, University of Wisconsin-Madison
2022-11-11 15:00 - 16:00 | Zoom Event, MI, United States
WATCH THE RECORDING HERE.
Jennifer Franck is an Assistant Professor in the Department of Engineering Physics at the University of Wisconsin-Madison. She leads the Computational Flow Physics and Modeling Lab, using computational fluid dynamics (CFD) techniques to explore the flow physics of unsteady and turbulent flows. Ongoing research projects are in the areas of bio-inspired flows and the fluid dynamics of renewable energy systems with current projects funded by NSF and ARPA-E. Prior to joining the UW-Madison faculty in 2018, she was faculty at Brown University. She received her undergraduate degree in Aerospace Engineering from University of Virginia, followed by a M.S. and Ph.D. from California Institute of Technology. Following her PhD, she was awarded an NSF Postdoctoral Fellowship hosted at Brown University to computationally explore fluid dynamics mechanics of flapping flight.
PREDICTIVE MODELING OF OSCILLATING FOIL WAKE DYNAMICS
Swimming and flying animals rely on the fluid around them to provide lift or thrust forces, leaving behind a distinct vortex wake in the fluid. The structure and size of the vortex wake is a blueprint of the animal’s kinematic trajectory, holding information about the forces and also the size, speed and direction of motion. This talk will introduce a bio-inspired oscillating turbine, which can be operated to generate energy from moving water through lift generation, in the same manner as flapping birds or bats. This style of turbines offers distinct benefits compared with traditional rotation-based turbines such as the ability to dynamically shift its kinematics for changing flow conditions, thus altering its wake pattern. Current efforts lie in predicting the vortex formation and dynamics of the highly structured wake such that it can be utilized towards cooperative motion within arrays of oscillating foils. Using numerical simulations, this talk will discuss efforts towards linking the fluid dynamic wake signature to the underlying foil kinematics, and investigating how that effects the energy harvesting performance of downstream foils. Two machine learning methodologies are introduced to classify, cluster and identify complex vorticity patterns and modes of energy harvesting, and inform more detailed modeling of arrays of oscillating foils.

The MICDE Fall 2022 Seminar Series is open to all. University of Michigan faculty and students interested in computational fluid dynamics are encouraged to attend.
This seminar is cohosted by the Michigan Institute for Computational Discovery and Engineering (MICDE) and the Applied & Interdisciplinary Mathematics program (AIM) at the University of Michigan. Prof. Franck will be hosted by Prof. Silas Alben, Professor of Mathematics.
This is a virtual event broadcasted online via Zoom.
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 Seminar: Miguel Bessa Associate Professor of Engineering, Brown University
2022-11-16 15:30 - 16:30 | 1303 EECS, 1301 Beal Ave, Ann Arbor, MI, 48109, United States
Miguel Bessa is an Associate Professor in the School of Engineering at Brown University. His research interests include computational mechanics and materials science, development of numerical methods, machine learning and optimization, multi-scale modeling of materials and structures. Miguel Bessa and his research group envision a new era for the design of materials and structures using artificial intelligence. Miguel received a PhD in Mechanical Engineering from Northwestern University in 2016 as a Fulbright scholar. After a short postdoctoral position at Caltech (2017) and a quick leap from Assistant to Associate Professor (2021) at Delft University of Technology, he joined the Solid Mechanics Group at Brown University in the Summer of 2022.
COOPERATIVE DATA-DRIVEN MODELING
The human brain is capable of learning tasks mostly without forgetting. However, deep neural networks suffer from catastrophic forgetting when learning tasks one after the other. We address this challenge considering a class-incremental learning scenario where the network sees test data without knowing its origin. We show the best results to date for the ImageNet dataset, outperforming by more than 20% the state of the art. The proposed method is also applied to learn material laws, illustrating its versatility. This strategy is believed to open new avenues for cooperation among different researchers and practitioners.

The MICDE Fall 2022 Seminar Series is open to all.
This seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Bessa will be hosted by Prof. Krishna Garikipati, Professor of Mechanical Engineering and Mathematics and Director of MICDE.
This is an in-person event, Zoom link will only be provided upon request.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
Questions? Email MICDE-events@umich.edu
WATCH THE RECORDING HERE.

MICDE Seminar: Mark Vogelsberger Associate Professor of Physics, Massachusetts Institute of Technology
2023-01-17 14:00 - 15:00 | MI
WATCH THE RECORDING HERE
Professor Vogelsberger grew up in Germany, and received his undergraduate degree in physics from the University of Mainz and his Ph.D from the University of Munich and the Max Planck Institute for Astrophysics in 2010, advised by Prof. Simon D.M. White. In 2009 he won the Rudolf Kippenhahn Prize for his thesis work. He was an ITC postdoctoral fellow at the Harvard-Smithsonian Center for Astrophysics from 2009-2012, and a Hubble fellow from 2012-2013. In 2014, Dr. Vogelsberger joined the MIT physics faculty as Assistant Professor. In 2016 he won an Alfred P. Sloan Fellowship in Physics. Professor Vogelsberger was promoted to Associate Professor in 2018. Professor Vogelsberger is a theoretical astrophysicist whose research interests broadly cover structure and galaxy formation, dark matter physics and large-scale hydrodynamical simulations. He makes extensive use of numerical simulations using state-of-the-art high-performance supercomputers around the world.

SIMULATING EARLY STRUCTURE AND GALAXY FORMATION – THE THESAN PROJECT
Cosmological simulations of galaxy formation have evolved significantly over the last decade. In my talk I will describe recent efforts to model the
large-scale distribution of galaxies with cosmological hydrodynamical simulations. The focus of the talk will be a discussion of our new simulation
campaign, the THESAN project, to study the epoch of re-ionization and the early Universe.

The MICDE Winter 2023 Seminar Series is open to all. University of Michigan faculty and students interested in cosmology are encouraged to attend.
This seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Vogelsberger will be hosted by Prof. Monica Valluri, Research Professor of Astronomy.
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 Seminar: Albert Berahas Assistant Professor of Industrial and Operations Engineering at the University of Michigan
2023-01-23 15:00 - 16:00 | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States
Albert S. Berahas is an Assistant Professor in the Industrial and Operations Engineering department at the University of Michigan. Before joining the University of Michigan, he was a Postdoctoral Research Fellow in the Industrial and Systems Engineering department at Lehigh University working with Professors Katya Scheinberg, Frank Curtis and Martin Takáč. Prior to that appointment, he was a Postdoctoral Research Fellow in the Industrial Engineering and Management Sciences department at Northwestern University working with Professor Jorge Nocedal. Berahas completed his PhD studies in the Engineering Sciences and Applied Mathematics (ESAM) department at Northwestern University in 2018, advised by Professor Jorge Nocedal. He received his undergraduate degree in Operations Research and Industrial Engineering (ORIE) from Cornell University in 2009, and in 2012 obtained an MS degree in Applied Mathematics from Northwestern University. Berahas’ research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.

ALGORITHMS FOR DETERMINISTICALLY CONSTRAINED STOCHASTIC OPTIMIZATION
Stochastic gradient and related methods for solving stochastic optimization problems have been studied extensively in recent years. It has been shown that such algorithms and much of their convergence and complexity guarantees extend in straightforward ways when one considers problems involving simple constraints, such as when one can perform projections onto the feasible region of the problem. However, settings with general nonlinear constraints have received less attention, and many of the approaches that have been proposed for solving such problems resort to using penalty or (augmented) Lagrangian methods, which are often not the most effective strategies. In this work, we propose and analyze stochastic optimization algorithms for deterministically constrained problems based on the sequential quadratic optimization (commonly known as SQP) methodology. We discuss the rationale behind our proposed techniques, convergence in expectation and complexity guarantees for our algorithms, and the results of preliminary numerical experiments that we have performed. This is joint work with Raghu Bollapragada, Frank E. Curtis, Michael O’Neill, Daniel P. Robinson, Jiahao Shi and Baoyu Zhou.

The MICDE Winter 2023 Seminar Series is open to all.
This seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Berahas will be hosted by Prof. Siqian Shen, Associate Professor of Industrial and Operations Engineering and Associate Professor of Civil and Environmental Engineering.
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 Seminar: Mark Pauly Professor of Computer Science, École Polytechnique Fédérale de Lausanne
2023-02-16 12:00 - 13:00 | MI
WATCH THE RECORDING HERE.
Mark Pauly is a full professor at EPFL, where he directs the Geometric Computing Laboratory (GCM). Prior to joining EPFL, he was assistant professor at ETH Zurich, postdoctoral scholar at Stanford University, and doctoral student at ETH Zurich. He received the ETH medal for outstanding dissertation in 2003, was awarded the Eurographics Young Researcher Award in 2006, an ERC Starting Grant in 2010, and the Eurographics Outstanding Technical Contributions Award in 2016. He is the co-founder of two EPFL spin-offs, Faceshift AG and Rayform SA.

COMPUTATIONAL INVERSE DESIGN OF DEPLOYABLE STRUCTURES
Research at the EPFL Geometric Computing Laboratory (GCM) aims at empowering creators. We develop efficient simulation and optimization algorithms to build computational design methodologies for advanced material systems and digital fabrication technology. Mathematical reasoning, geometric abstractions, and powerful numerical methods are key ingredients in our work.
In this talk I will show how these tools can be used to solve challenging inverse problems for deployable structures that can transition between multiple geometric states. Several design studies will highlight how the interplay of geometry, computation, and digital fabrication technologies facilitates the discovery of new material systems with superior functional performance. Such systems offer a wide variety of potential applications, for example in industrial and consumer products, soft robotics, medical devices, or architecture.

The MICDE Winter 2023 Seminar Series is open to all. University of Michigan faculty and students interested in computationally designed advanced material systems and digital fabrication technology are encouraged to attend.
This seminar is hosted by the Michigan Institute for Computational Discovery & Engineering (MICDE). Prof. Pauly will be hosted by Prof. Evgueni Filipov, Assistant Professor of Civil and Environmental Engineering.
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 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

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: Olivier Desjardins, Professor of Mechanical and Aerospace Engineering at Cornell University
2023-10-02 16:00 - 17:00 | 1109 FXB, 1320 Beal Ave., Ann Arbor, MI, United States
Bio: Olivier Desjardins is a Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University. He joined the Cornell MAE faculty in July 2011. Prior to that, he was on the Mechanical Engineering faculty at the University of Colorado at Boulder. He received a Master of Science in Aeronautics and Astronautics from ENSAE (Supaero) in Toulouse, France, in 2004. The same year, he received a Master of Science in Mechanical Engineering from Stanford University, then in 2008 he obtained a Ph.D. in Mechanical Engineering from Stanford University. He received an NSF CAREER award in 2014 to work on turbulence modeling around liquid-gas interfaces, and he was presented with the Junior Award from the International Conference on Multiphase Flow in 2016.
Research Interests: Prof. Desjardins’ research focuses on large-scale numerical modeling of turbulent reacting multiphase flows with industrial application. Using world-class parallel computers, his group develops numerical methods and models to investigate the multi-scale and multi-physics fluid mechanics problems that arise in a range of engineering devices, such as combustors or biomass reactors.
High-fidelity computational techniques such as large-eddy simulations and direct numerical simulations are at the heart of Dr. Desjardins’ research. By enabling the exploration of complex non-linear flow physics from first principles, these techniques have the potential to guide the development of highly optimized energy and propulsion systems.
Multi-scale modeling of topology change in multiphase flow simulations
Liquid atomization and spray formation are ubiquitous processes in nature as well as engineered system. Predicting droplet size distributions from first principle simulations presents a fantastic challenge due to the wide range of scales involved in topology change. In this talk, we present new developments to the geometric volume of fluid method that enable the tracking of sub-grid scale interfacial features. By reconstructing the interface with multiple planar surfaces or with paraboloid surfaces, we show that ligaments and sheets can be represented accurately independently of mesh resolution while preserving exact conservation, good computational efficiency, and easy integration with finite-volume-based flow solvers. A consequence of such strategies is that lack of mesh resolution no longer induces topology change, which then needs to be reintroduced explicitly using physics-based models. We discuss various flavors of such models in the context of the break-up of thin liquid films, a common feature in aerodynamic liquid atomization.

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). Prof. Desjardins will be hosted by Dr. Jesse Capecelatro, Associate Professor of Mechanical and Aerospace 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

LANL XPS Seminar: Mark W. Schraad, Division Leader for Computational Physics at Los Alamos National Laboratory
2023-10-05 14:00 - 15:00 | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States
Bio: Mark W. Schraad earned his Ph.D. in Aerospace Engineering from the University of Michigan and has nearly three decades of research and development experience at Los Alamos National Laboratory. He focused his research career on materials physics, with specialization in structured materials and material instabilities, while also gaining scientific leadership experience across theoretical and computational physics, modeling and simulation, and scientific software development for advanced computing architectures and hardware. Mark has balanced experience across Los Alamos Science, Technology, and Engineering and Weapons Directorates, and across the Laboratory’s basic science and mission application portfolios. In his current position, he serves as Division Leader for Computational Physics within the Weapons Physics Directorate at Los Alamos National Laboratory. In this role, he is responsible for the development and delivery of LANL’s suite of mission-critical modeling and simulation software, which is used in the design, certification, and assessment of the U.S. nuclear stockpile.
High-Performance Computing and the Future of Big Science for Department of Energy Applications
Los Alamos is the birthplace of computational physics and has been at the forefront of high-performance computing for nearly eight decades. Integrating physics theory and advanced numerical methods in the instantiation of multi-physics software has allowed Los Alamos to address a broad range of science and technology applications. Today, as one of 17 Department of Energy National Laboratories, Los Alamos continues to develop and deploy advanced software in the execution of a complex mission across national security, energy security, and environmental and climate science. As part of that endeavor, the Computational Physics Division at Los Alamos develops and delivers a continuously evolving suite of production software products to design and analyze large-scale integrated physics experiments and to enable the design, assessment, and confident certification of the U.S. nuclear stockpile. These software products are deployed on leading-edge, high-performance computing platforms, such as the Trinity and Crossroads supercomputers at Los Alamos, and the Sierra and El Capitan machines at Lawrence Livermore National Laboratory. With a shifting geopolitical landscape, our software serves a national security mission of ever-increasing importance. Yet, simultaneously, the rapid pace of science and technology change places a premium on agility, with a diversity of computing platforms and architectures coming online, and with AI poised to revolutionize approaches to modern science. Ultimately, an integration of artificial intelligence methodologies with the co-design of software and future computing architectures will allow new levels of physics fidelity, numerical accuracy, and efficiency in time to solution for the most challenging scientific workflows to address a broad spectrum of future, big-science problems.

Snacks and refreshments will be provided!

MICDE / LANL T Division - James Patrick Colgan, Deputy Division Leader Los Alamos National Laboratory Theoretical Division
2023-10-11 11:00 - 12:00 | Lurie Robert H. Engin. Ctr – Johnson Rooms (LEC 3213)
Join us to learn more about the Theoretical Division of Los Alamos National Laboratory. Are you familiar with the Oppenheimer movie?
You can also hear about the exciting opportunities available for graduate students and post docs at LANL.
Speaker: James Patrick Colgan, Deputy Division Leader Los Alamos National Laboratory

Bio: James Colgan is the Deputy Division Leader of Theoretical Division at Los
Alamos National Laboratory. James received his BSc and PhD degrees in Theoretical
Physics from Queen’s University, Belfast, Northern Ireland. After a post-doctoral
position at Auburn University, he joined LANL in 2003 as a post-doctoral researcher
and was converted to a staff scientist position in 2005 in Theoretical Division. James
became Group Leader of the Physics and Chemistry of Materials (T-1) in 2017 and
became Deputy Division Leader in 2022. James has published extensively in atomic
and plasma physics and was elected a Fellow of the American Physical Society (APS)
in 2012 and a Fellow of the U.K. Institute of Physics (IOP) in 2021
An overview of Los Alamos National Laboratory and the Theoretical Division
Abstract: An overview of the activities of Los Alamos National Laboratory (LANL)
are presented. LANL was founded in 1943 under the leadership of J. Robert
Oppenheimer to direct the “Manhattan Project” – a top-secret project to create the
atomic bomb. Now 80 years later, in 2023, LANL is tasked by the nation through the
Department of Energy and National Nuclear Security Administration to deliver
national security solutions to address the issues faced by the nation and world.
LANL achieves its mission by applying multidisciplinary science, technology and
engineering capabilities using unique experimental, computational, and nuclear
facilities.
This overview will provide a brief survey of LANL’s activities and then will focus on
the research & development portfolio of LANL’s Theoretical (T) Division (part of the
Directorate for Simulation & Computation). T Division, which has existed since the
inception of LANL, aims to provide excellence in basic and applied theoretical
research across many disciplines, notably computational materials science and the
development of cutting-edge computational tools to support the national security
mission of the Laboratory.

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.

MICDE Faculty seminar: Philip Roe, Emeritus Professor, Aerospace Engineering U-M
2023-12-07 15:00 - 16:00 | 1109 FXB, 1320 Beal Ave., Ann Arbor, MI, United States
Zoom link
Bio: Philip Roe is an Emeritus Professor of Aerospace Engineering at the University of Michigan. He is recognized for his pioneering work in the field of Computational Fluid Dynamics and Magnetohydrodynamics. Roe made many fundamental contributions to the development of high-resolution schemes for hyperbolic conservation laws. He is best known for his work on the flux difference splitting for compressible flows with shocks, typically referred to as the Roe scheme.
Musings of a Computational Philosopher
Philosophy sets a great story by asking the right questions. Indeed a correct answer to the wrong question is seldom of any value. You can even obtain tenure, I am told, by asking questions that you cannot yet answer. For the past decade, I have been trying to ask the right questions about computing compressible flow, and I hope here to provide a glimpse of the answers.
A “good” algorithm should “obviously” be accurate, cheap, and robust. Of these three desiderata, I will try here to clarify the notion of accuracy. Although clearly a good thing, it is almost always defined asymptotically in terms of the behavior at small mesh size or low frequency. This sets precise goals for analysis, and although accuracy can be achieved in this sense, in practice we often cannot afford the asymptotic regime. Moreover, when we deal with compressible flow, we are forced to deal with high frequencies. We require in fact only rather modest accuracy at low frequency, but must extend this into the high frequency regime, and doing this will require answering different questions. I will discuss a double-pronged approach to finding these questions and their answers.
This approach demands that the information flow in the computer should closely match that in real life. A great advance toward this was made by introducing Godunov-type methods, but these merely distinguish left from right, and their reliance on one-dimensional physics has many drawbacks. However, for many kinds of problem there are integral solutions to the linear initial-value problem in multiple dimensions. My first prong is to show how these can be used to derive algorithms for linear and nonlinear problems for compressible fluid flow and other applications. These algorithms have remarkable properties, including true incompressible limits and automatic boundary conditions. The information flow is different for advective and non-advective modes of the solution.
As a second way to achieve correct information flow, I employ solution derivatives as degrees of freedom. This Hermitian representation is common in computer graphics and signal processing but almost unknown in CFD. Its great benefit consists of keeping the stencil compact. This brings about sharp discontinuities, extends the spectrum and reduces communication overheads. Recently, with my graduate student Iman Samani, I have used both prongs of my approach to produce fifth-order solutions for linear elastodynamics on unstructured grids with automatic handling of material interfaces and remote boundaries. I will present these results, summarize what remains to be done and describe some target applications.

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

MICDE/ MCAIM seminar: Boyce Griffith, Professor at the University of North Carolina
2024-04-12 15:00 - 16:00 | East Hall – 1084
Bio: Boyce Griffith is a Professor in the Department of Mathematics and Department of Biomedical Engineering at the University of North Carolina, where he is also an Adjunct Professor of Applied Physical Sciences and Associate Chair for Research in the Department of Mathematics. His research group focuses on the development and application of numerical methods for simulating fluid-structure interaction with a particular focus on models of the heart and its valves. Their core approach is based on extensions of the immersed boundary method fluid-structure interaction.
Immersed methods for fluid-structure interaction
The immersed boundary (IB) method is a framework for modeling systems in which an elastic structure interacts with a viscous incompressible fluid. The fundamental feature of the IB approach to such fluid-structure interaction (FSI) problems is its combination of an Eulerian formulation of the momentum equation and incompressibility constraint with a Lagrangian description of the structural deformations and resultant forces. In conventional IB methods, Eulerian and Lagrangian variables are linked through integral equations with Dirac delta function kernels, and these singular kernels are replaced by regularized delta functions when the equations are discretized for computer simulation. This talk will focus on three related extensions of the IB method. I first detail an IB approach to structural models that use the framework of large-deformation nonlinear elasticity. I will focus on efficient numerical methods that enable finite element structural models in large-scale simulations, with examples focusing on models of the heart and its valves. Next, I will describe an extension of the IB framework to simulate soft material failure using peridynamics, which is a nonlocal structural mechanics formulation. Numerical examples demonstrate constitutive correspondence with classical mechanics for non-failure cases along with essentially grid-independent predictions of fluid-driven soft material failure. Finally, I will introduce a reformulation of the IB large-deformation elasticity framework that enables accurate and efficient fluid-structure coupling through a version of the immersed interface method, which is a sharp-interface IB-type method. Computational examples demonstrate the ability of this methodology to simulate a broad range of fluid-structure mass density ratios without suffering from artificial added mass instabilities, and to facilitate subgrid contact models. I will also present biomedical applications of the methodology, including models of clot capture by inferior vena cava filters.

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), the Department of Mathematics and the Michigan Center for Applied and Interdisciplinary Mathematics (AIM).
This is an in-person event.
Graduate Certificate in Computational Discovery and Engineering, and MICDE fellows, please use this form to record your attendance.
