MICDE Seminar: Mattia Gazzola, Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign
2019-01-18 15:00 - 16:00 (America/Detroit) | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Mattia Gazzola is an Assistant Professor in the department of Mechanical and Science Engineering at the University of Illinois, Urbana-Champaign. Originally from Galliate, Italy, he obtained his B. Sc. in Energy Engineering and M. Sc. in Nuclear Engineering from the Politecnico di Milano. He then was granted a PhD. in Mechanical Engineering from ETH Zurich where he worked with Prof. Petros Koumoutsakos specializing in simulation, optimization and learning of artificial swimmers. His research interests include locomotion in fluids, where he combines theory, numerical simulations and AI to advance our understanding of the physical mechanisms involved. He is also interested in creating artificial animals – or cyborgs – to link neuro-dynamics, mechanics and complex controllable gaits-coupling sensory information to motor coordination and movement that leads to behavior. His research group develops numerical algorithms that allow the integration of AI with large scale simulations.
MODELING, SIMULATION AND CONTROL OF COMPLEX MUSCULOSKELETAL ARCHITECTURES
We introduce a modeling approach based on assemblies of Cosserat rods for the simulation and characterization of arbitrary muscoloskeletal architectures. The obtained solver is coupled to evolutionary optimization techniques for the rational design of soft artificial creatures characterized by different scales and operating across environments. A control approach for these distributed mechanical systems is outlined and demonstrated in simple settings. Applications range from slithering, swimming and flying biolocomotion strategies to bio-hybrid systems.
This is a joint seminar with the Applied and Interdisciplinary Mathematics seminar series. Prof. Gazzola is being hosted by Prof. Alben (Mathematics). If you would like to meet with him during his visit, please send an email to micde-events@umich.edu.

MICDE Seminar: Yuri Bazilevs, School of Engineering, Brown University
2019-01-11 15:00 - 16:00 | 2540 G.G. Brown (2350 Hayward St.), 2300 Hayward St, Ann Arbor, MI, 48109, United States
Bio: Yuri Bazilevs is the E. Paul Sorensen Chair in the School of Engineering at Brown University. He was previously a Professor and Vice Chair in the Structural Engineering Department at the University of California, San Diego. Yuri is the original developer of Isogeometric Analysis (IGA), a new computational methodology that aims to integrate engineering design (CAD) and simulation (FEM). For his research contributions Yuri received a number of awards and honors, including the 2018 ASCE Walter L. Huber Research Prize. He is included in the 2014-2018 lists of Highly Cited Researchers, both in the Engineering and Computer Science categories.
ISOGEOMETRIC METHODS FOR SOLIDS, STRUCTURES, AND FLUID-STRUCTURE INTERACTION: FROM EARLY RESULTS TO RECENT DEVELOPMENTS
This presentation is focused on Isogeometric Analysis (IGA) with applications to solids and structures, starting with early developments and results, and transitioning to more recent work. Novel IGA-based thin-shell formulations are discussed, and applications to progressive damage modeling in composite laminates due to low-velocity impact and their residual-strength prediction are shown. Fluid–structure interaction (FSI) employing IGA is also discussed, and a novel framework for air-blast-structure interaction (ABSI) based on an immersed approach coupling IGA and RKPM-based Meshfree methods is presented and verified on a set of challenging examples. The presentation is infused with examples that highlight effective uses of IGA in advanced engineering applications.
Prof. Bazilevs is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet him during his visit please send an email to micde-events@umich.edu. If you are an MICDE or ME student and would like to join Prof. Bazilevs for lunch please RVSP here by Wednesday, January 9.

MICDE Seminar: Aaron Frank, Chemistry and Biophysics, University of Michigan
2018-12-12 16:00 - 17:00 | 1210 Chemistry & Willard H Dow Laboratory, 930 University Ave., Ann Arbor, MI, 48109, United States
Bio: Aaron Frank is originally from Grenada, a small island in the Caribbean. After moving to the US in 2001, Aaron received his BA in chemistry from Brooklyn College in 2006, where he carried out research in the groups of Professors Charlene Forest, Shaneen Singh, and Alexander Greer. He then moved to Michigan to attend graduate school at the University of Michigan and then, with his Ph.D advisor Professor Ioan Andricioaei, moved to UC Irvine in 2008. Aaron received his Ph.D in chemistry in 2011. Following a 2 year stint at Nymirum Inc. — a small biotech company in Ann Arbor founded by a close collaborator, Professor Hashimi Al-Hashimi — he returned to the University of Michigan as a Presidential Postdoctoral Fellow where he was mentored by Professor Charles L. Brooks, III. Aaron is now an Assistant Professor at the University of Michigan in the Chemistry Department and the Biophysics Department.
DATA SCIENCE AT THE INTERFACE OF BIOLOGY, CHEMISTRY, AND PHYSICS
In this talk, I will describe examples of how my research group uses data science tools to tackle research problems that fall at the interface between Biology, Chemistry, and Physics. First, I will describe ongoing research focused on mapping the structure-landscape of functional ribonucleic acids (or RNAs). In this project, we combined machine learning and secondary structure modeling tools to predict the structure of RNAs conditioned on available NMR chemical shift data. This method now enables us to model individual conformational states, including previously invisible states of an RNA, based on its sequence and available chemical shift data. Second, I will describe ongoing research centered around decoding structure-kinetic relationships (SKRs) in sparse datasets. There is now immense interest in developing drugs that exhibit elevated residence times on their target. In this project, we used machine learning to encapsulate SKRs for CDK2, a prominent cancer target, from a dataset containing only fourteen (14) samples. I will describe our efforts to build and test CDK2-specific SKR models that take as input, the atomic structure of receptor-ligand complexes and output estimates of their residence times. Additionally, I will describe proof-of-concept studies that demonstrate the utility of our CDK2-specific SKR models as tools to help efficiently explore chemical space in search of novel chemical scaffolds that are enriched with high-residence time and potent inhibitors of CDK2.

MICDE Seminar: Youssef Marzouk, Department of Aeronautics and Astronautics, MIT
2018-12-03 16:00 - 17:00 | 107 Gorguze Family Laboratory, 2609 Draper Dr, Ann Arbor, MI, 48109, United States
Bio: Youssef Marzouk is an associate professor in the Department of Aeronautics and Astronautics at MIT, and co-director of the MIT Center for Computational Engineering. He is also director of MIT’s Aerospace Computational Design Laboratory.
His research interests lie at the intersection of physical modeling with statistical inference and computation. In particular, he develops methodologies for uncertainty quantification, inverse problems, large-scale Bayesian computation, and optimal experimental design in complex physical systems. His methodological work is motivated by a wide variety of engineering, environmental, and geophysics applications.
He received his SB, SM, and PhD degrees from MIT and spent several years at Sandia National Laboratories before joining the MIT faculty in 2009. He is a recipient of the Hertz Foundation Doctoral Thesis Prize (2004), the Sandia Laboratories Truman Fellowship (2004-2007), the US Department of Energy Early Career Research Award (2010), and the Junior Bose Award for Teaching Excellence from the MIT School of Engineering (2012). He is an Associate Fellow of the AIAA and currently serves on the editorial boards of the SIAM Journal on Scientific Computing, Advances in Computational Mathematics, and the SIAM/ASA Journal on Uncertainty Quantification. He is also an avid coffee drinker and classical pianist.
A TOUR OF TRANSPORT METHODS FOR BAYESIAN COMPUTATION
Bayesian inference provides a natural framework for quantifying uncertainty in parameter estimates and model predictions, and for combining heterogeneous sources of information. Characterizing the results of Bayesian inference—by simulating from the posterior distribution—often proceeds via Markov chain Monte Carlo or sequential Monte Carlo sampling, but remains computationally challenging for complex posteriors and large-scale models.
This talk will describe a broad framework for using measure transport in Bayesian computation. This framework seeks deterministic couplings of the posterior measure with a tractable “reference” measure (e.g., a standard Gaussian). Such couplings are induced by transport maps, and enable direct simulation from the desired measure simply by evaluating the transport map at samples from the reference. Approximate transports can also be used to “precondition” and accelerate standard Monte Carlo schemes. Within this framework, one can describe many useful notions of low-dimensional structure associated with inference: for instance, sparse or decomposable transports underpin modeling and computation with non-Gaussian Markov random fields, and low-rank transports arise frequently in inverse problems.
We will then describe recent work specializing transport maps to the problem of nonlinear filtering in high-dimensional state-space models. The idea is to transform a forecast ensemble into samples from the current filtering distribution via a sequence of nonlinear transport maps, computed via convex optimization. Construction of the maps is regularized by leveraging potential structure in the filtering problem—e.g., decay of correlations, approximate conditional independence, and local likelihoods—thus extending notions of localization to nonlinear updates. The proposed framework can be understood as a non-Gaussian generalization of the ensemble Kalman filter.
This is joint work with Alessio Spantini, Daniele Bigoni, Ricardo Baptista, and Matthew Parno.
Prof. Marzouk is being hosted by Prof. Duraisamy (Aerospace). If you would like to meet him during his visit please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. Marzouk for lunch please RVSP here by Friday, November 30.

CANCELLED --MICDE Seminar: Ali Yilmaz, Electrical Engineering, University of Texas at Austin
2018-11-26 15:00 - 16:00 | 1311 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
CANCELLED
Bio: Ali Yilmaz is an Associate Professor of Electrical and Computer Engineering and a core faculty member at the Institute for Computational Engineering and Sciences at the University of Texas at Austin.
Dr. Yilmaz received the Ph.D. degree in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign in 2005. He spent 2005 to 2006 as a post-doctoral research associate with the Center for Computational Electromagnetics at the University of Illinois; in 2006, he joined the faculty of The University of Texas at Austin.
His research interests include computational electromagnetics (particularly fast frequency- and time-domain integral equation solvers), parallel algorithms, antenna and scattering analysis, bioelectromagnetics, geoelectromagnetics, and electronic packages. He has authored or co-authored over 170 papers in refereed journals and international conferences on these topics.
USING (SUPER) COMPUTERS JUDICIOUSLY FOR HIGHER FIDELITY ELECTROMAGNETIC ANALYSIS
Increasing the fidelity of the electromagnetic models generally increases the predictive power of the analyses based on the models. It also generally increases the results’ sensitivity to model features/parameters as well as the difficulty of constructing the models, accurately solving the governing equations, and interpreting the resulting data. Therefore, one should base the analysis on the lowest-fidelity model one can get away with or, equivalently, the highest-fidelity model one can afford. The sweet spot for the tradeoff, “the appropriate model”, has changed over time in part because past successes in simulation-based science and engineering have increased expectations/requirements from electromagnetic analysis and in part because tremendous improvements in computing infrastructure and advances in computational methods have increased the affordability of complex analysis. Finding the appropriate model requires understanding both the benefits and the costs of analysis when a lower- or higher-fidelity model is used; neither side of the ledger, however, is known beforehand (unless one is repeating previously established analyses). A possible approach to revealing these unknowns is to construct models by gradually increasing their fidelity, performing analysis at each fidelity level, and comparing the analysis results and costs to those from the previous steps. I will show examples of this “analysis-driven modeling” in bioelectromagnetics (using the AustinMan and AustinWoman human body models) and signal integrity (using an electronic package example) by employing parallel algorithms and advanced integral-equation solvers on leading-edge supercomputers.
The examples will highlight many of the challenges arising from this approach to modeling. An important one is that “the appropriate method” of analysis generally depends on the model, e.g., a method can outperform alternatives for low-fidelity models but underperform them for high-fidelity ones; indeed, inappropriate (but convenient) methods can not only inflate the cost side of the ledger but also deflate the benefit side, leading to misjudgment of the appropriate model fidelity. Thus, not surprisingly, the development of appropriate electromagnetic models and appropriate computational methods are tightly linked (aka “if all you have is a hammer, everything looks like a nail”). Unfortunately, evaluating computational methods to find the appropriate one for a given model is surprisingly difficult, even for unbiased experts, as method performances depend not just on the models but also on the computers, the software realizations of the methods, and the users/developers of the software. On the one hand, theoretical comparisons (e.g., of asymptotic complexities, error convergence rates, parallel scalability limits) are often incapable of factoring in the large impact of software and hardware infrastructure on the realized/observed performance of a computational method—a problem that has worsened as the traditional Dennard scaling of clock frequencies ended in the last decade. On the other hand, empirical comparisons are beset by the same problems that physical measurements face (including irreproducible and uncertain results), require many (potentially low-efficiency) computations, and suffer from the large number of alternative methods. I will discuss whether benchmark suites can improve the judicious use of computational methods for electromagnetic analysis and what the necessary ingredients for such benchmarks are.
Prof. Yilmaz is being hosted by Prof. Michielssen (EECS). If you would like to meet with him during his visit, please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. Yilmaz for lunch, please fill out this form.

MICDE/IOE Seminar: Juan Pablo Vielma, Sloan School of Management, MIT
2018-10-24 16:00 - 17:00 | 1680 IOE, 1205 BEAL AVE, Ann Arbor, MI, 48109, United States
Bio: Juan Pablo Vielma is the Richard S. Leghorn (1939) Career Development Associate Professor at MIT Sloan School of Management and is affiliated to MIT’s Operations Research Center. Dr. Vielma has a B.S. in Mathematical Engineering from University of Chile and a Ph.D. in Industrial Engineering from the Georgia Institute of Technology. His current research interests include the theory and practice of mixed-integer mathematical optimization and applications in natural resource management, marketing and statistics. In January of 2017 he was named by President Obama as one of the recipients of the Presidential Early Career Award for Scientists and Engineers (PECASE). Some of his other recognitions include the NSF CAREER Award, the INFORMS Computing Society Prize and a first prize in the INFORMS Junior Faculty Interest Group Paper Competition. He served as vice-chair of Integer and Discrete Optimization for the INFORMS Optimization Society and as chair of the INFORMS Section on Energy, Natural Resources, and the Environment. He is currently an associate editor for Operations Research and Operations Research Letters, a member of the NumFocus steering committee for JuMP, and the Faculty Director for the MIT-Chile program of MIT’s International Science and Technology Initiatives (MISTI).
Modeling power of mixed integer convex optimization problems and their effective solution with Julia and JuMP
More than 50 years of development have made mixed integer linear programming (MILP) an extremely successful tool. MILP’s modeling flexibility allows it describe a wide range of business, engineering and scientific problems, and, while MILP is NP-hard, many of these problems are routinely solved in practice thanks to state-of-the-art solvers that nearly double their machine-independent speeds every year. Inspired by this success, the last decade has seen a surge of activity on the solution and application of mixed integer convex programming (MICP), which extends MILP’s versatility by allowing the use of convex constraints in addition to linear inequalities. In this talk we cover various recent developments concerning theory, algorithms and computation for MICP. Solvers for MICP can be significantly more effective than those for more general non-convex optimization, so one of the questions we cover in this talk is what classes of non-convex constraints can be modeled through MICP. We also cover the solution of MICP problems through polyhedral approximation algorithms that exploit the power of extended formulations. Finally, we cover various topics concerning the modeling and computational solution of MICP problems using the Julia programming language and the JuMP modeling language for optimization. In Particular, we show how mixed integer optimal control problems where the variables are polynomials can be easily modeled and solved by seamlessly combining several Julia packages and JuMP extensions with the Julia-written MICP solver Pajarito.
This seminar is co-sponsored by the department of Industrial and Operations Engineering. Prof. Vielma is being hosted by Prof. Shen (IOE). If you would like to meet with him during his visit, please send an email to micde-events@umich.edu

MICDE/Quantitative Biology Seminar: Padmini Rangamani, Mechanical and Aerospace Engineering, UC San Diego
2018-10-22 12:00 - 13:00 | 335 West Hall, 1085 S University, Ann Arbor, MI, 48109, United States
Bio: Padmini Rangamani is an associate professor in Mechanical Engineering at the University of California, San Diego. She joined the department in July 2014. Earlier, she was a UC Berkeley Chancellor’s Postdoctoral Fellow, where she worked on lipid bilayer mechanics. She obtained her Ph.D. in biological sciences from the Icahn School of Medicine at Mount Sinai. She received her B.S. and M.S. in Chemical Engineering from Osmania University (Hyderabad, India) and Georgia Institute of Technology respectively. She is the recipient of the ARO, AFOSR, and ONR Young Investigator Awards, and a Sloan Research Fellowship for Computational and Molecular Evolutionary Biology. She is also the lead PI for a MURI award on Bioinspired low energy information processing from the AFOSR.
GEOMETRIC PRINCIPLES OF SPATIO-TEMPORAL DYNAMICS OF SECOND MESSENGERS IN DENDRITIC SPINES
The ability of the brain to encode and store information depends on the plastic nature of the individual synapses. The increase and decrease in synaptic strength, mediated through the structural plasticity of the spine, are important for learning, memory, and cognitive function. Dendritic spines are small structures that contain the synapse. They come in a variety of shapes (stubby, thin, or mushroom-shaped) and a wide range of sizes that protrude from the dendrite. These spines are the regions where the postsynaptic biochemical machinery responds to the neurotransmitters. Spines are dynamic structures, changing in size, shape, and number during development and aging. While spines and synapses have inspired neuromorphic engineering, the biophysical events underlying synaptic and structural plasticity remain poorly understood.
Our current focus is on understanding the biophysical events underlying structural plasticity. I will discuss two recent efforts from my group — first, a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium influx caused by NMDA receptor activation and second, a series of spatial models to study the role of spine geometry and organelle location within the spine for calcium and cyclic AMP signaling. I will conclude with some new efforts in using reconstructions from electron microscopy to inform computational domains. I will conclude with how geometry and mechanics plays an important role in our understanding of fundamental biological phenomena and some general ideas on bio-inspired engineering.
Prof. Rangamani is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet with her please send an email to micde-events@umich.edu. If you are an MICDE student and would like to join Prof. Rangamani for lunch please fill out this form

MICDE Seminar: Nandini Ananth, Department of Chemistry and Chemical Biology, Cornell University
2018-10-09 16:00 - 17:00 | CHEM 1300, 930 N University Ave, Ann Arbor, MI, 48109, United States
Bio: Nandini Ananth is an associate professor in the department of Chemistry and Chemical Biology at Cornell University. She received her bachelor’s degree in Chemistry from Stella Maris College in Chennai, India, and a Masters in chemistry from the Indian Institute of Technology Madras. Nandini moved to the United States in the fall of 2003 to pursue doctoral research at the University of California, Berkeley in William Miller’s group, working on developing semiclassical methods to model quantum dynamical behavior in complex chemical reactions. Upon graduation, she accepted a position as postdoctoral scholar in Thomas Miller’s group at the California Institute of Technology, Pasadena, where her research focused on developing path-integral methods for the simulation of electronically nonadiabatic processes in the condensed phase. She joined the faculty of the department of Chemistry and Chemical Biology at Cornell University in the Fall of 2012, and during her time here has received the Cottrell Scholar Award, NSF CAREER Award, NSF EAGER Award, Sloan Research Fellowship, and Army Research Office’s Young Investigator Award.
Charge Transfer Dynamics, Excited State Energetics, and Organic Photovoltaics
Designing molecular materials for use as organic photovoltaics, molecular electronics, and photocatalysts is a multifaceted challenge requiring a detailed understanding of both the excited state energetics and the dynamics of charge and energy transfer. We address the dynamic challenge by developing new methods based on the path integral formulation of quantum mechanics that are uniquely suited to the simulation of photo-initiated excited state dynamics in the condensed phase. We then tackle the characterization of the excited state manifold in molecular systems using a combination of high-level electronic structure methods to accurately calculate excited state energies, normal mode analysis to quantify vibronic couplings, and novel orbital analyses to uncover structure-spectrum correlations.
In this talk, we focus on one target application: designing chromophores that exhibit ultrafast Singlet Fission (SF), a phenomenon that has the potential to significantly increase organic solar cell efficiency. We investigate SF in non-bonded and covalently bonded pentacene dimers: we uncover two distinct mechanistic pathways for ultrafast SF and we identify molecular geometries and bonding motifs that can be modified to enhance efficiency in each case. Finally, we combine the insights obtained from our theoretical investigations to generate a priori design principles for next-generation SF chromophores, and working with experimental collaborators, we verify them.
Prof. Ananth is being hosted by Prof. Geva (Chemistry). If you would like to meet her during her visit please send an email to micde-events@umich.edu

MICDE Seminar: Pavel Bochev, Center for Computing Research, Sandia National Laboratories
2018-10-05 15:00 - 16:00 | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Pavel Bochev is a Distinguished Member of the Technical Staff at Sandia National Laboratories in Albuquerque where he works in the Center for Computing Research. He joined Sandia in 2000 after six years of teaching and research at the University of Texas at Arlington.

Pavel’s research interests include compatible discretizations for partial differential equations, optimization and control problems, and the development of new, property preserving heterogeneous numerical methods for complex applications relevant to the mission of the US Department of Energy and the National Nuclear Security Administration.

Pavel’s thesis was awarded the SIAM Student paper prize in 1994. In 2012 he was elected a Fellow of the Society for Industrial and Applied Mathematics. Pavel is a recipient of 2014 US Department of Energy’s E. O. Lawrence Medal in the category of “Computer, information and knowledge sciences”. This award honors U.S. scientists and engineers, at mid-career, for exceptional contributions in research and development supporting the Department of Energy and its mission to advance the national, economic and energy security of the United States. In 2017 Pavel was awarded the Thomas J.R. Hughes Medal by the U.S. Association for Computational Mechanics for his contributions to the field of numerical partial differential equations.

Pavel has authored and co-authored over 100 research papers, two books and several book chapters, and has given numerous plenary and invited lectures in the US and abroad. He served two terms as Editor-in-Chief of the SIAM Journal on Numerical Analysis and is currently member of the editorial board of SINUM.
Compatible Mesh-Free Methods
Particle and mesh-free methods offer significant computational advantages in settings where quality mesh generation required for many compatible PDE discretizations may be expensive or even intractable. At the same time, the lack of underlying geometric grid structure makes it more difficult to construct mesh-free methods mirroring the discrete vector calculus properties of mesh-based compatible and mimetic discretization methods. In this talk we survey ongoing efforts at Sandia National Laboratories to develop new classes of locally and globally compatible meshfree methods that attempt to recover some of the key properties of mimetic discretization methods.
We will present two examples of recently developed “mimetic”-like meshfree methods. The first one is motivated by classical staggered discretization methods. We use the local connectivity graph of a discretization particle to define locally compatible discrete operators. In particular, the edge-to-vertex connectivity matrix of the local graph provides a topological gradient, whereas a generalized moving least-squares (GMLS) reconstruction from the edge midpoints defines a divergence operator. The second method can be viewed as a meshfree analogue of a finite volume type scheme. In this method, the metric information that would be normally provided by the mesh, such as cell volumes and face areas, is reconstructed algebraically, without a mesh. This reconstruction process effectively creates virtual cells having virtual faces and ensures a local conservation property matching that of mesh-based finite volumes. In contrast to similar recent efforts our approach does not involve a solution of a global optimization problem to find the virtual cell volumes and faces areas. Instead, we determine the necessary metric information by solving a graph Laplacian problem that can be effectively preconditioned by algebraic multigrid.
Several numerical examples will illustrate the mimetic properties of the new meshfree schemes. The talk will also review some of the ongoing work to build a modern software toolkit for mesh-free and particle discretizations that leverages Sandia’s Trillinos library and performance tools such as Kokkos.
This is a joint seminar with the Applied and Interdisciplinary Mathematics program. Dr. Bochev is being hosted by Prof. Robert Krasny (Mathematics). If you would like to meet with him, please send an email to micde-events@umich.edu

MICDE Seminar: Sanjay Padhi, AWS Research and Technical Computing
2018-10-01 16:00 - 17:00 | 1670 Bob and Betty Beyster Building, 2260 Hayward Street, Ann Arbor, MI, 48109, United States
Bio: Dr. Sanjay Padhi, leads the AWS Research Initiatives including AWS’s federal initiatives with the National Science Foundation. He is a physicist and Adjunct Professor at Brown University. Dr. Padhi has more than 15 years of experience in large-scale distributed computing, Data Analytics and Machine Learning. He is the co-creator of the Workload Management System currently used for all the data processing and simulations by CMS, one of the largest experiments in the world at CERN, consisting of more than 180 institutions across 40 countries. He also co-founded the ZEUS Computing Grid project at Deutsches Elektronen-Synchrotron (DESY), Germany before joining CERN. Sanjay obtained his Ph.D from McGill University in High Energy Physics, co-author of more than 900 publications and is also currently appointed by the Dean of Faculty as an Adjunct Professor of Physics at Brown University.
Predictive Analytics using Amazon Web Services
One of the most explored features of Big Data is predictive analytics. Predictive analytics is a set of techniques that are fundamental to large organizations like Amazon. Methods such as Machine Learning are used in many aspects of life, including health care, education, financial modeling, and marketing. Analytics on Big Data has given rise to various “smart” projects, such as Connected Intersections, Smart Cities, and Smart Health. This talk will provide a range of such studies using predictive analytics including detailed overview of methods such as Machine Learning (ML) and Deep Learning using AWS. Fully managed Artificial Intelligence (AI) services to help researchers build, train and deploy ML models in various domains including Computer Vision and Natural Language Processing (NLP) will also be outlined. Supervised and unsupervised based learning frameworks and its implications in the fields of Scientific Computing, Medical Imaging, Cancer detection, Diabetic Retinopathy, and Voice-enabled solutions to improve management of chronic disease will be discussed. The AWS Research Initiative with funding agencies such as the National Science Foundation (NSF) in the domains related to the foundation and innovative tracks, as well as AWS Research Credit program will also be outlined.
If you would like to meet Dr. Sanjay Padhi on October 1, please send a request to micde-events@umich.edu

MICDE/EEB Seminar: Murat Eren, Department of Medicine, University of Chicago
2018-09-13 16:00 - 17:00 | MI
Bio: Dr. Murat Eren is an Assistant Professor in the department of Medicine and affiliated with the Marine Biological Laboratory at the University of Chicago. He received his B.S. from Canakkale Onsekiz Mart University in Turkey in 2002, and his PhD from the University of New Orleans in 2001, both in computer science. His research focuses on the diversity and functioning of microbial communities in environments ranging from the human gastrointestinal tract and oral cavity, to sewages, oceans, and soils. He designs algorithms and experiments to better understand microbes and their ecology. He pursues interesting ecological and evolutionary questions, with some particularly interesting insights from molecular data into what constitutes a population in the microbial world.
Insights into ecology and evolution of microbial populations through single-amino acid variants
Neither the mechanisms by which genomic heterogeneity emerges within naturally occurring microbial populations, nor how it drives the partitioning of ecological niches are well understood. Yet the increasing number of environmental metagenomes with astonishing depth of sequencing offer new opportunities to investigate evolutionary processes acting upon them, and link genomic variation to predicted tertiary structures of genes to gain biochemical insights.
MICDE is co-sponsoring this seminar with the department of Ecology and Evolutionary Biology. If you would like to meet Dr. Murat during his visit please send an email to micde-events@umich.edu

MICDE Seminar: Vladimir Druskin, Scientific Advisor, Schlumberger Doll Research
2018-04-16 16:00 - 17:00 | 1360 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Vladimir Druskin is an applied mathematician with expertise in several areas including numerical algorithms, large scale numerical simulations, computational linear algebra, inverse problems, model reduction, computational geophysics, subsurface and medical imaging, electrical engineering and financial mathematics. Dr. Druskin got his Ph.D. from Lomonosov Moscow State University (MSU) focused on applied mathematics. He is currently a scientific advisor at Schlumberger Doll Research working in energy research and development with demonstrated successful history of leading large collaborative industrial-academic projects in mathematical modeling and data-processing.
Reduced order models, networks, and applications to modeling and imaging with waves
Geophysical seismic exploration, as well as radar and sonar imaging require the solution of large-scale forward and inverse problems for hyperbolic systems of equations. In this talk, I will show how model order reduction can be used to address some intrinsic difficulties of these problems. In model order reduction, one approximates the response (transfer function) of a large-scale dynamical system using a smaller system, called the reduced order model (ROM). We consider ROMs that capture properties of the large problem that are essential for imaging and that can be realized via sparse graph-Laplacian networks. The ROMs are data-driven, i.e., they learn the underlying PDE problem from the transfer function. One of the better-known applications of our ROMs is the efficient discretization of PDE problems in unbounded domains. Here I will focus on two recent applications: (i) Multiscale modeling of elastic wave propagation via network approximations, with low communication and computational cost; (ii) A direct, nonlinear acoustic imaging algorithm in strongly heterogeneous media, where the ROM is used to manipulate the data in such a way that multiply scattered waves are separated from the single scattered ones.
Dr. Druskin is being hosted by Prof. Borcea (Mathematics) and Prof. Schotland (Mathematics & Physics). If you would like to meet him, please send an email to micde-contact@umich.edu

MICDE Seminar: Raul Radovitzky, Department of Aeronautics and Astronautics, Massachusetts Institute of Technology
2018-04-10 14:30 - 15:30 | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States
Bio: Raul Radovitzky is the Raymond L. Bisplinghoff Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. He also serves as the Associate Director of the MIT Institute for Soldier Nanotechnologies, where he also leads research efforts on Blast and Ballistic Protection. He received a Civil Engineer degree from the University of Buenos Aires in 1991, A S. M. in Applied Mathematics from Brown University in 1995 and a Ph.D. in Aeronautical Engineering from the California Institute of Technology in 1998. His research interests are in the development of numerical methods for multi-scale modeling of complex material response as well as in the formulation and implementation of algorithms for large-scale simulation of the dynamic response of materials to extreme loading conditions with emphasis on material and structural failure. The methods his group has developed have led to significant advances in our understanding of the physical effects of blast waves on the brain. This has helped to develop strategies to protect against Traumatic Brain Injury. Dr. Radovitzky is an Associate Fellow of the American Institute of Aeronautics and Astronautics and a member of the National Football League Head, Neck and Spine Injury Research Committee.
Extension of the peridynamic theory of solids for the simulation of materials under extreme loadings
The prediction of material and structural failure remains one of the most difficult challenges in structural and solid mechanics. Complexity emerges from the fundamental multiscale aspect of the mechanics of fracture, where the small-scale response is usually responsible for large-scale system damage and failure. In addition, significant algorithmic challenges remain, including the difficulty in representing fracture, some fundamental numerical convergence issues in the presence of material damage; and computational robustness and scalability enabling large-scale simulations.
In this presentation, I will describe our efforts on the investigation of the theory of peridynamics and its numerical implementation, as a promising alternative approach for simulating extreme material response. Peridynamics is a relatively new, nonlocal formulation of continuum mechanics based on integral equations. It includes a physical length scale and naturally supports the presence of discontinuities in the solution field. As part of our work in this area, we have proposed an extended formulation of the state theory of peridynamics addressing some fundamental issues present in the original theory. Specifically, we have found that unphysical energy modes that do not contribute to the strain energy are allowed in the original formulation, which, in turn, are responsible for the numerical instabilities commonly observed in peridynamic particle discretizations. In order to address this issue, we introduce an extension of the constitutive correspondence framework based on bond-level nonlinear strain measures of the Seth-Hill type, in direct analogy to local measures of deformation in continuum mechanics. We show that the numerical instabilities are eliminated when the numerical discretization is based on the extended theory.
In addition, we have explored different approaches for incorporating material damage and fracture within the context of peridynamics formulations. I will describe one approach based on continuum damage models and another one particularly suited for brittle fracture.
The algorithms resulting from a particle discretizations of the proposed extended peridynamics framework have been implemented in our research code ΣMIT. I will provide examples illustrating the key numerical properties of the method. In addition, I will show numerical results that demonstrate the ability of the method to capture experimentally observed ballistic limit curves for ductile materials, as well as realistic fracture patterns in brittle materials subjected to projectile impact loadings.
Prof. Radovitzky is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet with him, please send an email to micde-contact@umich.edu

CEE/MICDE Seminar: Khachik Sargsyan, Sandia National Laboratories
2018-04-06 15:00 - 16:00 | 1311 EECS, 1301 Beal Ave., Ann Arbor, MI, 48109, United States
Bio: Khachik Sargsyan is a Principal Member of Technical Staff at Sandia National Laboratories (SNL) in Livermore, CA. Before staff and postdoctoral positions at SNL, he received his Ph.D. in Applied and Interdisciplinary Mathematics from University of Michigan, Ann Arbor, in 2007. His Bachelors degree, awarded in 2002, is in Applied Math and Physics from Moscow Institute of Physics and Technology. Dr. Sargsyan’s research evolves around uncertainty quantification (UQ) and predictability analysis of physical and computational models. He has developed and applied methods for model reduction, UQ and data assimilation, targeting fundamental challenges such as structural errors, intrinsic stochasticity, high-dimensionality, limited data, discontinuities and rare events, with applications in climate modeling, chemical kinetics, hardware architecture simulators and turbulent combustion. He is one of the lead developers of UQTk (www.sandia.gov/uqtoolkit), a lightweight C++/Python software toolkit for quantification of uncertainties in model predictions.

Dr. Sargsyan is being hosted by Prof. Ivanov (Civil and Env. Engineering). If you would like to meet him, please send an email to Chase Dwelle at dwellem@umich.edu
Probabilistic Methods for Uncertainty Quantification in Computational Models
Over the last decade, improved measurement capabilities and computational resources have led to significant algorithmic developments toward efficient uncertainty quantification (UQ) for computational models. Such models of physical systems often involve input parameters that exhibit certain degree of uncertainty. Estimation and propagation of these uncertainties are crucial for model validation, computational/experimental design and decision making. ​This talk will focus on probabilistic methods with emphasis on Polynomial Chaos (PC) expansions as a means for functional representation of random variables. The talk will highlight the use of PC methods both for forward propagation of uncertainties and for inverse problems, such as parameter estimation via Bayesian inference. I will list associated major challenges, including the curse of dimensionality and model structural error estimation, in the context of computationally expensive models of physical systems. Both fundamental and more recent methods will be introduced and demonstrated, impacting a wide range of applications, such as climate modeling, turbulent combustion and chemical kinetics.

AIM Seminar: Christoph Börgers, Mathematics, Tufts University
2018-04-06 15:00 - 16:00 | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Christoph Börgers is a Professor of Mathematics at Tufts University. He got his Ph.D. under Prof. Charles Peskin at the Courant Institute of Mathematical Sciences, in 1985. Prof. Börgers was a professor in the University of Michigan department of Mathematics until 1996 when he moved to Tufts. His expertise is in mathematical neuroscience, applied dynamical systems, numerical analysis, scientific computing, and during the past decade, most of his work has been in the area of Computational Neuroscience.
Rhythms in neuronal networks with recurrent excitation
Interacting excitatory and inhibitory neuronal populations often generate oscillations in electrical fields in the brain. I will briefly review this mechanism and the reasons to believe that it is important in brain function. Most of the talk will be focused on the effects of recurrent excitation, i.e., of the neurons of a local network in the brain exciting each other. Recurrent excitation can sustain activity in a network that would otherwise be quiescent; this is believed to be the basis of working memory. It can also lead to a runaway process, with excitation generating more excitation etc., much as the presence of a quadratic term on the right-hand side of a differential equation can lead to blow-up in finite time; this may be related to epileptic seizures. For model problems, we prove that abrupt transitions to runaway activity require recurrent excitation with fast kinetics, while working memory activity is more robust with recurrent excitation with slow kinetics.
Prof. Börgers is being hosted by Prof. Robert Krasny (Mathematics).

MICDE Seminar: Amanda Randles, Department of Biomedical Engineering, Duke University
2018-04-02 14:00 - 15:00 | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States
Bio: Amanda Randles is an assistant professor of Biomedical Engineering at Duke University. She has courtesy appointments in the departments of Mechanical Engineering and Material Science, Computer Science and Mathematics, and is a member of the Duke Cancer Institute. She got her Ph.D. from Harvard University in 2013, and has been the recipient of the Lawrence Fellowship (Lawrence Livermore National Lab.), the Anita Borg Memorial Scholarship (Google), and the George Michael Memorial High Performance Computing Fellowship (ACM/IEEE) among many accomplishments in her early career. Her research in biomedical simulation and high performance computing focuses on the development of new computational tools that she uses to provide insight into the localization and development of human diseases ranging from atherosclerosis to cancer.
Massively Parallel Simulations of Hemodynamics in the Human Vasculature
The recognition of the role hemodynamic forces have in the localization and development of disease has motivated large-scale efforts to enable patient-specific simulations. When combined with computational approaches that can extend the models to include physiologically accurate hematocrit levels in large regions of the circulatory system, these image-based models yield insight into the underlying mechanisms driving disease progression and inform surgical planning or the design of next generation drug delivery systems. Building a detailed, realistic model of human blood flow, however, is a formidable mathematical and computational challenge. The models must incorporate the motion of fluid, intricate geometry of the blood vessels, continual pulse-driven changes in flow and pressure, and the behavior of suspended bodies such as red blood cells. In this talk, I will discuss the development of HARVEY, a parallel fluid dynamics application designed to model hemodynamics in patient-specific geometries. I will cover the methods introduced to reduce the overall time-to-solution and enable near-linear strong scaling on up to 1,572,864 core of the IBM Blue Gene/Q supercomputer. Finally, I will present the expansion of the scope of projects to address not only vascular diseases, but also treatment planning and the movement of circulating tumor cells in the bloodstream.
Prof. Randles is being hosted by Dr. Carrasco-Teja (MICDE). If you would like to meet her during her visit please send an email to mcteja@umich.edu

MICDE Seminar: Michael Falk, Department of Materials Science and Engineering, Johns Hopkins University
2018-03-30 14:00 - 15:00 | 1303 EECS, 1301 Beal Ave, Ann Arbor, MI, 48109, United States
Bio: Michael Falk is a professor of Materials Science and Engineering at Johns Hopkins University where he also serves as the Vice Dean for Undergraduate Education. He holds a bachelor’s degree in physics and a master’s degree in Computer Science from Johns Hopkins. He completed his Ph.D. in physics at the University of California, Santa Barbara and then launched his academic career as a computational materials scientist at the University of Michigan in 2000. In 2008 he returned to Johns Hopkins as an associate professor of Materials Science and Engineering with joint appointments in Mechanical Engineering and Physics. Prof. Falk’s research focuses on utilizing computer simulation on the atomic scale to understand the processes by which materials are pushed out of equilibrium by processes such as bending, breaking, charging and undergoing frictional sliding. His research has had an abiding focus on the ways glass structures accommodate plastic flow, deformation and fracture. These investigations have involved developing new methodologies for deploying molecular dynamics simulations and the development of thermodynamically motivated constitutive theories. Prof. Falk also engages in educational research and is a strong advocate for diversity and inclusion, engaging in outreach to Baltimore City elementary schools and advocating for a welcoming climate for LGBTQ people within the engineering and physics professions.
Connecting atomistic simulations, defect-based theories and continuum plasticity in amorphous solids
Glasses, and the more general category of materials known as amorphous solids, lack crystal structure and find wide application from consumer goods to photovoltaics. Yet, issues quantifying disorder have stymied the construction of physically grounded mechanical constitutive laws for these materials suitable for failure prediction. Atomistic simulation methods can provide some insight regarding the mechanisms of plastic deformation and strain localization. Recent investigations have aimed at quantifying the defects that control plastic flow by quantifying a yield stress field at the nanometer scale. Analysis of these fields have confirmed some of the assumptions built into the shear transformation zone theory of amorphous plasticity, most notably the orientational nature of the defect and their pre-existence in the structure. I will further discuss methods for quantitatively predicting strain localization, a limiting failure process in high-strength metallic glasses and other amorphous materials by parameterizing the effective-temperature shear transformation zone theory from molecular dynamics simulations. We have directly cross-compared molecular dynamics simulations and continuum representations of these same materials in order to test and validate our constitutive theories. The role of coarse graining in the linkage of continuum and atomistic methods is crucial, and convergence only arises above a critical length scale on the order of tens of angstroms. The investigation makes clear the need to separate out the relevant fluctuations in material structure from the shorter wavelength fluctuations that serve to obscure them. It is, in the end, the interactions between these larger-scale relevant fluctuations via the material’s mechanical response that controls the failure process during strain localization.
Prof. Falk is being hosted by Prof. Yue Fan (Mechanical Engineering). If you would like to meet him during his visit please email micde-contact@umich.edu

Computation: A Pillar of Science and a Lens to the Future — the 2018 MICDE Symposium
2018-03-22 08:00 - 17:00 | Rackham Amphitheatre, 915 E. Washington St., Ann Arbor, MI, 48109, United States
The Michigan Institute for Computational Discovery and Engineering 2018 Symposium will feature eminent scientists from around the world and the U-M campus. The symposium this year will show how computational science is leading the research at all scales in our lives, from the molecular level to the sky.
Visit the Symposium page for more details.
Please register if you plan to attend.

SPEAKERS

Guruduth Banavar
Chief Technology Officer
Viome

Cynthia Chestek
Assistant Professor, Biomedical Engineering and EECS
University of Michigan

Alison Marsden
Principal Investigator, Cardiovascular Biomechanics Computation Lab
Stanford University

Cleve Moler
Cofounder and Chief Mathematician
MathWorks

Raju Namburu
Chief, Computational and Information Sciences Directorate
Army Research Lab

Stephen Smith
Assistant Professor, Ecology and Evolutionary Biology
University of Michigan

Beth Wingate
Professor, Mathematics
University of Exeter

POSTER COMPETITION
The symposium will include a poster competition highlighting outstanding computational work from U-M students and postdocs. First place is awarded $500, and second and third places win $250.

MICDE Seminar: Michael Shelley, Courant Institute, New York University
2018-03-09 15:00 - 16:00 | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Michael J. Shelley is an American applied mathematician who works on the modeling and simulation of complex systems arising in physics and biology. He holds a BA in Mathematics from the University of Colorado (1981) and a PhD in Applied Mathematics from the University of Arizona (1985). He was a postdoctoral researcher at Princeton University, and then joined the faculty of mathematics at the University of Chicago. In 1992 he joined the Courant Institute of Mathematical Sciences at New York University where he is the George and Lilian Lyttle Professor of Applied Mathematics. He is also a Professor of Neuroscience (NYU) and Professor of Mechanical Engineering (NYU-Poly).
Professor Shelley’s work includes free-boundary problems in fluids and materials science, singularity formation in partial differential equations, modeling visual perception in the primary visual cortex, dynamics of complex and active fluids, cellular biophysics, and fluid-structure interaction problems such as the flapping of flags, stream-lining in nature, and flapping flight. He is also the co-founder and co-director of the Courant Institute’s Applied Mathematics Lab.
Source https://en.wikipedia.org/wiki/Michael_Shelley_(mathematician)
Modeling and Simulating Active Mechanics in the Cell
Many fundamental phenomena in eukaryotic cells — nuclear migration, spindle positioning, chromosome segregation — involve the interaction of (often transitory) cellular structures with boundaries and fluids. Understanding the consequences of these interactions require specialized numerical methods for their large-scale simulation, as well as mathematical modeling and analysis. In this context, I will discuss the recent interactions of mathematical modeling and large-scale, detailed simulations with experimental measurements of activity-driven Biomechanical processes within the cell.

MICDE Seminar: Heather Mayes, Chemical Engineering, University of Michigan
2018-02-20 14:00 - 15:00 | NCRC10 ACR2, 2800 Plymouth Rd, Ann Arbor, MI, 48109, United States
Bio: Heather Mayes is an Assistant Professor in the Department of Chemical Engineering. Her research group uses multi-scale modeling to discover protein-sugar interactions and to harness them for renewable energy and improved health. The study of carbohydrate-protein interactions is an important step to create renewable fuels and chemicals from non-food biomass, and the results can be applied to several human diseases, including cancer and autoimmune disorders. Prof. Mayes uses computational tools in her research, including quantum mechanics, molecular dynamics, and rare-event sampling methods. She collaborates with experimental groups to understand past and guide future wet-lab studies to advance renewable chemicals and fuels, as well as disease understanding.
Simulating Protein-Carbohydrate Interactions to Bridge the Gap Between Human Chemical Intuition and Molecular Biophysics
In complex reacting systems, it can be exceedingly difficult, or even impossible, to tease out elementary reaction mechanisms from wet-lab data alone, due to data convolution resulting from the multiple reacting steps and competing reactions that simultaneously occur. The systems that the Mayes group studies (multiple types of protein-carbohydrate interactions) certainly fall into this category, with understanding further hindered by the conformational, stereochemical, and regiochemical degrees of freedom key to chemical reactions in these systems. Yet, understanding these elementary mechanisms would not only help answer fundamental questions in biology, but also improve our ability to harness these systems for applications from renewable energy to pharmaceutical interventions. I will discuss several systems that we are studying, and focus on our investigations of how enzymes break down plant biomass. I will share how our computational research rationalizes non-intuitive wet-lab observations by revealing mechanisms that do not conform to human intuition. In doing so, we gather lessons from how nature has evolved efficient enzymes that we can then apply to rational enzyme design.

MICDE Seminar: Dominika Zgid, Chemistry, University of Michigan
2018-02-08 14:00 - 15:00 | CHEM 1706, 930 N University, Ann Arbor, MI, 48109, United States
Bio: Dominika Zgid is an assistant professor of Chemistry at the University of Michigan. She received her Ph.D. from the University of Waterloo, Canada, in 2008. Since starting at Michigan, she has received a DOE Early Career Award in 2013 and an NSF Career Award in 2015.
Her main interests are at the interface of theoretical chemistry and condensed matter physics with a major focus on designing new, systematically improvable and controlled computational methods that can be used to study strongly correlated molecules and materials. She has worked on a variety of topics, such as a molecular version of density matrix renormalization group, solvers for dynamical mean field theory using explicit bath formulation, conserving Green’s function methods for weakly correlated systems and the development of the self-energy embedding theory.
Towards Accurate Quantum-Mechanical Calculations beyond Density Functional Theory on Large Systems
We present a detailed discussion of self-energy embedding theory (SEET) which is a quantum embedding scheme allowing us to describe a chosen subsystem very accurately while keeping the description of the environment at a lower cost. We apply SEET to molecular examples where commonly our chosen subsystem is made out of a set of strongly correlated orbitals while the weakly correlated orbitals constitute an environment. Such a self-energy separation is very general and to make this procedure applicable to multiple systems a detailed and practical procedure for the evaluation of the system and environment self-energy is necessary. We list all the intricacies for one of the possible procedures while focusing our discussion on many practical implementation aspects such as the choice of best orbital basis, impurity solver, and many steps necessary to reach chemical accuracy.
Finally, on a set of carefully chosen molecular examples, we demonstrate that SEET, which is a controlled, systematically improvable Green’s function method can be as accurate as established wavefunction quantum chemistry methods.

MICDE Seminar: Jesse Capecelatro, Department of Mechanical Engineering, University of Michigan
2018-01-24 15:00 - 16:00 | 185 EWR, 1351 Beal Ave, Ann Arbor, MI, 48109, United States
Bio: Professor Capecelatro is interested in developing large-scale simulation capabilities for prediction and design of the complex multi-physics and multiphase flows relevant to energy and the environment. To achieve this, his group develops robust and scalable numerical methods to leverage world-class supercomputing resources. His current research projects are focused on adjoint-based methods applied to turbulent combustion, modeling strongly-coupled particle-laden flows, and understanding interactions between electrostatics and turbulence in atmospheric clouds.
Prior to joining the mechanical engineering department at the University of Michigan in 2016, Dr. Capecelatro was a research scientist at the Center for Exascale Simulation of Plasma-coupled Combustion (XPACC) at the University of Illinois Urbana-Champaign. He received a B.S. in mechanical engineering from SUNY Binghamton in 2009, and two years later completed a M.S. in mechanical engineering from the University of Colorado Boulder, where he performed research in collaboration with the National Renewable Energy Laboratory on numerical modeling of fluidized bed reactors. In 2014 he received a Ph.D. from Cornell University under the guidance of Prof Olivier Desjardins, where his thesis focused on high performance computing of turbulent multiphase flows. He spent the summer following his Ph.D. as a visiting postdoc at the Institut de Mécanique des Fluides de Toulouse and École Centrale Paris focusing on fundamental and numerical studies of particle-induced turbulence.
Towards Accurate and Tractable Methods of Disperse Multiphase Flows in Extreme Environments
The complex and multiscale behavior associated with turbulent flows is further complicated by the presence of a disperse phase (i.e., solid particles, liquid drops, or gaseous bubbles). Strong coupling between the disperse phase and underlying turbulence plays important roles across engineering and science. For example, liquid sprays are often used during rocket launches to suppress undesirable aeroacoustic loading on the fuselage and nearby equipment. Recent experiments have shown that water sound suppression systems might also be a viable option for jet noise reduction during take-off and landing of high-performance aircrafts. Within the energy sector, turbulent suspensions of catalytic particles are used in a variety of energy conversion technologies, yet the multiphase dynamics occurring in these reactors remain largely unknown. A key challenge in understanding and predicting turbulent multiphase flows is the fundamental importance of processes occurring on extremely small scales that ultimately influence the macroscopic behavior. This presentation will provide an overview of recent advancements in numerical modeling of particle-laden flows with several applications of ongoing research projects.

MICDE Seminar: Theresa Windus, Chemistry, Iowa State University
2018-01-16 16:00 - 17:00 | CHEM 1640, 930 N University, Ann Arbor, MI, 48109, United States
Bio: Theresa Windus is a professor of Chemistry at Iowa State University. She earned her Ph.D. from Iowa State University in 1993 and did post-doctoral research at Northwestern University. Theresa was also the Director of Computational Chemistry/Training at Ohio Supercomputer Center and the Computational Chemistry lead at the Wright Patterson Air Force Base Major Shared Resource Center. Most recently, she was the manager of the Molecular Science Software Group and the Visualization and User Services group in the Molecular Science Computing Facility in the Environmental Molecular Sciences Laboratory of Pacific Northwest National Laboratory.
The challenges of the exascale from the view of a molecular chemist
This talk will focus on the challenges that computational chemistry faces in taking the equations that model the very small (molecules and the reactions they undergo) to efficient and scalable implementations on the very large computers of today andtomorrow. In particular, how do we take advantage of the newest architectures while preparing for the next generation of computers? How do we increase programmer productivity while ensuring excellent performance, efficiency and portability across multiple platforms? How do we take advantage of the work of mathematicians, computer scientists and other computational scientists to enable our science, while ensuring maintainability and usability of the software? How do we ensure that the algorithms that we develop are making wise use of the computational resources? How do help the next generation of computational chemists to be ready for the complex computing environments that they will face? While not claiming to have answers to all (or any!) of these questions, we will explore some possible solutions and their implications as we go forward and face the current petascale and the future exascale challenges. These will be in the context of several Department of Energy funded computational chemistry Exascale Computing Projects (NWChemEx and GAMESS) and the NSF funded Molecular Sciences Software Institute.
Prof. Windus is being hosted by Prof. Geva (Chemistry). If you would like to meet with him Prof. Windus during her visit please email mcteja@umich.edu

MICDE Seminar: Tarek Zohdi, Department of Mechanical Engineering, University of California, Berkeley
2017-12-05 15:00 - 16:00 | 1109 FXB, 1320 Beal Ave., Ann Arbor, MI, United States
Bio: Tarek I. Zohdi received his Ph.D. in 1997 in Computational and Applied Mathematics from the University of Texas at Austin and his Habilitation in General Mechanics from the Gottfried Leibniz University of Hannover in 2002. He is currently a Chancellor’s Professor of Mechanical Engineering, Chair of the Computational and Data Science and Engineering Program at UC Berkeley and holder of the W. C. Hall Family Endowed Chair in Engineering. He also holds a Staff Scientist position at Lawrence Berkeley National Labs. His main research interests are in computational approaches for advanced manufacturing and nonconvex multiscale-multiphysics inverse problems, in particular addressing the issue of how large numbers of micro-constituents interact to produce macroscale aggregate material behavior. He has published over 145 archival refereed journal papers and five books. In 2000, he received the Zienkiewicz Prize and Medal, which are awarded once every two years, to one post-graduate researcher under the age of 35, by The Institution of Civil Engineers in London, to commemorate the work of Professor O. C. Zienkiewicz, for research which contributes most to the field of numerical methods in engineering. In 2002, he received the Best Paper of the Year 2001 Award in London, at the Lord’s Cricket Grounds, for a paper published in Engineering Computations, pertaining to modeling and simulation of the propagation of failure in particulate aggregates of material. In 2003, he received the Junior Achievement Award of the American Academy of Mechanics. The award is given once a year, to one post-graduate researcher, to recognize outstanding research during the first decade of a professional career. In 2008, he was elected Fellow of the International Association for Computational Mechanics (IACM) and in 2009 he was elected Fellow of the United Stated Association for Computational Mechanics (USACM). He was elected President of the USACM in 2012, and served from 2012 to 2014. He is an editor of Computational Mechanics, Editor in Chief of Computational Particle Mechanics and serves on 12 editorial boards of international journals. For more information visit http://www.me.berkeley.edu/people/faculty/tarek-i-zohdi
Modeling and Simulation of Multistage Multiphysical Processes in Next-Generation Advanced Manufacturing and 3D Printing with New Multifunctional Materials
Within the last decade, several industrialized countries have stressed the importance of advanced manufacturing to their economies. Many of these plans have highlighted the development of additive manufacturing techniques, such as 3D printing, which are still in their infancy. The objective is to develop superior products, produced at lower overall operational costs. For these goals to be realized, a deep understanding of the essential ingredients comprising the materials involved in additive manufacturing is needed. The combination of rigorous material modeling theories, coupled with the dramatic increase of computational power can potentially play a significant role in the analysis, control, and design of many emerging additive manufacturing processes. Specialized materials and the precise design of their properties are key factors in the processes. Specifically, particle-functionalized materials play a central role in this field, in three main ways: (1) to endow filament-based materials by adding particles to a heated binder (2) to “functionalize” inks by adding particles to freely flowing solvents and (3) to directly deposit particles, as dry powders, onto surfaces and then to heat them with a laser, e-beam or other external source, in order to fuse them into place. The goal of these processes is primarily to build surface structures, coatings, etc., which are extremely difficult to construct using classical manufacturing methods. The objective of this presentation is to introduce the audience to basic techniques which can allow them to rapidly develop and analyze particulate-based materials needed in new additive manufacturing processes. This presentation is broken into two main parts: continuum and discrete element approaches. The materials associated with methods (1) and (2) are closely related types of continua (particles embedded in a continuous binder) and are treated using continuum approaches. The materials in method (3), which are of a discrete particulate character, are analyzed using discrete element methods.
Prof. Zohdi is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet with him please email mcteja@umich.edu

MICDE Seminar: Edward Maginn, Department of Chemical and Biomolecular Engineering, University of Notre Dame
2017-11-21 13:30 - 14:30 | MI
Bio: Edward Maginn received his BS in chemical engineering from Iowa State University and his PhD in chemical engineering from the University of California, Berkeley. Prior to attending graduate school, he worked as a process engineer for Procter and Gamble. He has been on the Notre Dame faculty since 1995 and currently holds the Dorini Family Chair of Energy Studies in the Department of Chemical and Biomolecular Engineering. He is also the chair of the department, and was formerly the Associate Dean for Academic Programs in the Graduate School. He has won a number of awards, including the Early Career Award from the Computational Molecular Science and Engineering Forum of the American Institute of Chemical Engineers, the ASEE Dow Outstanding New Faculty Award, the BP College of Engineering Outstanding Teacher Award and the NSF Career award. He is a Fellow of the American Association for the Advancement of Science and is a trustee of the CACHE Corporation. His research focuses on the development and use of atomistic molecular dynamics and Monte Carlo simulation methods to study the thermodynamic and transport properties of materials, with special emphasis on ionic systems important in energy storage and use.
Using Molecular Modeling to Design New Fluids for Energy Storage and Carbon Capture
Liquids that contain charged species, such as electrolytes and ionic liquids, have many important technological applications in fields such as energy storage, separations, and catalysis. By changing the structure of the molecules or employing mixtures, the properties of these fluids can be altered significantly. The key questions are: How should I change the structure of the molecule or ion to get the properties I want? What type of additives should I use to improve performance? To answer these and related questions, we use atomistic-level simulations to compute structural, thermodynamic and transport properties of these systems. We are able to provide molecular-level explanations for experimental observations, and we can predict properties of systems that may not yet have even been made in the laboratory.
In the first part of this talk, I will describe molecular modeling research directed at improving the performance of electrolytes used in next generation “beyond lithium” batteries. Electrolytes are a critical component of batteries, since they transport ions from the cathode to the anode during charging, then in the reverse direction in releasing energy on discharge. Electrolytes play a leading role in a battery’s capacity for energy storage, its lifetime and the safety of the battery. The electrolyte in a conventional lithium-ion battery consists of a lithium salt dissolved in an organic solvent. The electrolytes for next generation “beyond lithium” batteries will require new salt-solvent combinations. Our simulations probe the way in which different electrolyte formulations, charge carriers and additives impact the structure and dynamics of these liquids.
In the second half of the talk, I will show how these same kinds of simulations can be used to develop new ionic liquids that can be used for CO2 separations / capture. Ionic liquids are pure salts that are liquid at ambient temperatures. Because they have essentially no vapor pressure and readily dissolve CO2, people have been interested in using them for carbon capture. I will describe how our simulations have been successful in identifying new ionic liquids with properties tuned for use as conventional liquid absorbents or as supported ionic liquid membranes.
This is a joint seminar with the department of Chemical Engineering. Prof. Maginn is being hosted by Prof. Mayes (Chemical Engineering). If you are interested in meeting him during his visit please send an email to mcteja@umich.edu

MICDE Seminar: Chris Rycroft, Department of Applied Mathematics, Harvard University
2017-11-10 15:00 - 16:00 | 1084 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
Bio: Chris Rycroft is an Assistant Professor of Applied Mathematics in the School of Engineering and Applied Sciences at Harvard University. From 2010–2013, he was a Morrey Assistant Professor in the UC Berkeley Mathematics Department, and he was involved in the Bay Area Physical Sciences-Oncology where he collaborated with several experimental groups at Berkeley and UC San Francisco, on using computational modeling to understand the role of mechanical forces between cells and their environment. Prof. Rycroft’s research focuses on mathematical modeling and scientific computation, particularly for interdisciplinary applications in science and engineering. He works on a variety of problems, and has collaborated in a number of fields including physics, biology, materials science, and mechanical engineering. His current interests include questions that relate to the mechanics of materials, numerical algorithms, and geometry. Several of his recent projects relate to energy production and efficiency, such as modeling bulk metallic glasses, and developing high-throughput screening techniques to find advanced materials for carbon capture applications. He has also released several software libraries, including Voro++ for three-dimensional computations of the Voronoi tessellation.
The reference map technique for simulating complex materials and multi-body interactions
Conventional computational methods often create a dilemma for fluid-structure interaction problems. Typically, solids are simulated using a Lagrangian approach with grid that moves with the material, whereas fluids are simulated using an Eulerian approach with a fixed spatial grid, requiring some type of interfacial coupling between the two different perspectives. Here, a fully Eulerian method for simulating structures immersed in a fluid will be presented. By introducing a reference map variable to model finite-deformation constitutive relations in the structures on the same grid as the fluid, the interfacial coupling problem is highly simplified. The method is particularly well suited for simulating soft, highly-deformable materials and many-body contact problems, and several examples from engineering and biology will be presented. This is joint work with Ken Kamrin (MIT).
This is a joint seminar with the Interdisciplinary Applied Mathematics seminar series.
Prof. Rycroft is being hosted by Prof. Alben (Mathematics). If you would like to meet him please email Prof. Alben at alben@umich.edu or Dr. Mariana Carrasco-Teja at mcteja@umich.edu

MICDE Seminar: Giulia Galli, Department of Molecular Engineering, University of Chicago
2017-11-08 14:00 - 15:00 | MI
Bio: Giulia Galli is the Liew Family Professor of Electronic Structure and Simulations in the Institute for Molecular Engineering at the University of Chicago. She also holds a Senior Scientist position at Argonne National Laboratory (ANL) and she is a Senior Fellow of the UChicago/ANL Computational Institute. Prior to joining U Chicago and ANL, she was Professor of Chemistry and Physics at UC Davis (2005-2013) and the head of the Quantum Simulations group at the Lawrence Livermore National Laboratory (1998-2005).
She holds a Ph.D. in Physics from the International School of Advanced Studies (SISSA) in Trieste, Italy. She is a Fellow of the American Physical Society (APS) and of the AAAS. She is the recipient of an award of excellence from the Department of Energy (2000) and of the Science and Technology Award from the Lawrence Livermore National Laboratory (2004). She is currently the director of MICCoM (Midwest Integrated Center for Computational Materials), established by DOE in 2015. Her research activity is focused on the development and use of theoretical and computational tools to understand and predict the properties and behavior of materials (solids, liquids and nanostructures) from first principles.
Materials discovery and scientific design by computation: what does it take?
Substantial progress has been made in the last three decades in understanding and predicting the fundamental properties of materials and molecular systems from first principles, employing electronic structure methods and atomistic simulations. Using specific examples, I will discuss some predictions obtained for materials for energy conversion processes (photo-catalysis of water and solar cells) as well as some of the major challenges involved in enabling scientific discoveries by computation; in particular I will touch upon theoretical validation; and collection and verification of data generated by simulations. I will also discuss some of the theoretical and algorithmic advances required to broaden the scope of properties accessible by current ab initio simulations.
Professor Galli is being hosted by Prof. Siegel (Mechanical Engineering). If you would like to meet her during her visit please email mcteja@umich.edu

MICDE Seminar: Thomas Devereaux, Photon Science, Stanford University
2017-11-02 14:00 - 15:00 | 4448 East Hall, 530 Church St, Ann Arbor, MI, 48109, United States
Bio: Professor Devereaux received his Ph.D. in Physics from the University of Oregon in 1991, M.S. from University of Oregon in 1988, and B.S from New York University in 1986. Professor Devereaux is currently the Director of the Stanford Institute for Materials and Energy Sciences (SIMES), the Associate Lab Director (ALD) for Photon Science, a professor in the Photon Science Faculty at SLAC National Accelerator Laboratory and Stanford University and a Senior Fellow of the Precourt Institute for Energy. SIMES is a joint institute between Stanford main campus and SLAC, a national laboratory, focusing on scientific foundations related to the energy challenge facing our society. Professor Devereaux was a Post-doctoral Fellow at the Max Planck Institut, Stuttgart, (1991-1993), a Post-doctoral Fellow at the University of California, Davis, CA, (1993-1996), an Assistant Professor at The George Washington University, Washington, DC, (1996-1999), and an Associate Professor (1999-2006) and Professor (2006-2007) at the University of Waterloo, Waterloo, ON, Canada.
His main research interests lie in the areas of theoretical condensed matter physics and computational physics. His research effort focuses on using the tools of computational physics to understand quantum materials. Fortunately, we are poised in an excellent position as the speed and cost of computers have allowed us to tackle heretofore unaddressed problems involving interacting systems. The goal of his research is to understand electron dynamics via a combination of analytical theory and numerical simulations to provide insight into materials of relevance to energy science. His group carries out numerical simulations on SIMES’ high-performance supercomputer, the National Energy Research Scientific Computing Center (NERSC), and other US and Canadian computational facilities. The specific focus of the group is the development of numerical methods and theories of photon-based spectroscopies of strongly correlated materials.
Professor Devereaux’s awards include: U. S. Department of Education Fellowship (1989-1991); Junior Scholar Incentive Award, George Washington University (1998); Research Fellowship of the Alexander von Humboldt Foundation (2002-2006); Premier’s Research Excellence Award, Province of Ontario (2003); Scientist Research Fellowship, Embassy of France (2005); and Fellow of the American Physics Society (2008).
Light controlled topological phase transitions in multi-orbital and frustrated magnetic systems
Spurred by recent progress in melting, enhancement and induction of electronic order out of equilibrium, a tantalizing prospect concerns instead accessing transient Floquet steady states via broad pump pulses, to affect electronic properties. Here, we consider a two-pronged approach to manipulate the topology of a band insulator, as well as topological order in a Mott insulator. We first consider monolayer transition-metal dichalcogenides (TMDCs) [1], and show that their low-energy description as massive 2D relativistic fermions fails to hold for optical pumping. Instead, the added complexity of a realistic materials description leads to a novel mechanism to optically induce topologically-protected chiral edge modes, facilitating optically-switchable conduction channels that are insensitive to disorder. We develop a strategy to understand non-equilibrium Floquet-Bloch bands and topological transitions directly from ab initio calculations, and illustrate for the example of WS2 that control of chiral edge modes can be dictated solely from symmetry principles and is not qualitatively sensitive to microscopic materials details. Second, we extend these ideas to strongly correlated systems and show that pumping frustrated Mott insulators with circularly-polarized light can drive the effective spin system across a phase transition to a chiral spin liquid (CSL) [2]. We show that the transient time evolution of a Kagome lattice Hubbard model is well captured by an effective spin description, where circular polarization promotes a staggered scalar spin chirality Si . (Sj x Sk) directly to the Hamiltonian level. We fingerprint the ensuing phase diagram and find a stable photo-induced CSL in proximity to the equilibrium ground state. The results presented suggest new avenues to marry dynamical symmetry breaking, strong interactions, and ab initio materials modelling, to access elusive phase transitions that are not readily accessible in equilibrium.
References:
[1] M. Claassen et al, Nature Comm. 7, 13074 (2016).
[2] M. Claassen et al, arXiv:1611.07964, to appear in Nature Communications.
This is a joint CM Theory seminar. Prof. Devereaux is being hosted by Prof. Gull (Physics). If you are interested in meeting with him during his visit please send an email to mcteja@umich.edu

MICDE Seminar: Irina Tezaur, Extreme Scales Data Science and Analytics Department, Sandia National Laboratories
2017-10-25 15:00 - 16:00 | 1006 H.H. Dow, 2300 Hayward St, Ann Arbor, MI, 48109, United States
Bio: Dr. Irina Tezaur (f.k.a. Dr. Irina Kalashnikova) is a Principal Member of Technical Staff (PMTS) in the Extreme Scales Data Science & Analytics Department (Org. 8759) at Sandia National Laboratories in Livermore, CA. Prior to joining this group, from October 2011 to September 2014, she was SMTS in the Computational Mathematics Department (Org. 1442) at Sandia in Albuquerque, NM. She received her Ph.D. in Computational and Mathematical Engineering (CME) from Stanford University in 2011. Her advisor at Stanford was Professor Charbel Farhat and I was a member of the Farhat Research Group (FRG). Her Bachelors and Masters degrees are in pure mathematics, awarded by the University of Pennsylvania in 2006. Dr. Tezaur’s research interests are numerical solution to PDEs, mixed/hybrid finite element methods, stability and convergence properties of numerical methods, Reduced Order Modeling (ROM) and simulation-based analysis of fluid-structure interaction that she currently applies to climate modeling.
Next-generation modeling & simulation of large-scale ice sheets towards probabilistic sea-level change projections
Recent observations show that both the Greenland and Antarctic ice sheets are losing mass at increasingly rapid rates [1]. In its fourth assessment report (AR4), the Intergovernmental Panel on Climate Change (IPCC) declined to include estimates of future sea-level change from dynamics of the polar ice sheets due to the inability of ice sheet models to mimic or explain observed dynamic behaviors, such as the acceleration and thinning then occurring on several of Greenland’s large outlet glaciers [2]. In recent years, there has been a push to develop “next generation” land-ice models and codes for integration into global Earth System Models (ESMs). Unlike their predecessors, these codes: (1) are able to perform realistic, high-resolution, continental scale simulations, (2) are robust, efficient and scalable on next-generation hybrid systems (multi-core, many-core, GPU, Intel Xeon Phi), and (3) possess built-in advanced analysis capabilities (e.g., sensitivity analysis, optimization, uncertainty quantification). This talk will give an overview of the Albany/FELIX (Finite Elements for Land Ice eXperiments) [3] next-generation land-ice dynamical core (dycore) that is under development at Sandia National Laboratories as a part of a Department of Energy (DOE) SciDAC-funded project aimed at providing probabilistic sea-level projections from extreme-scale ice sheet and earth system models. This dycore is currently being integrated in to the DOE’s Acelerated Climate Model for Energy (ACME), which will be used to calculate anticipated 21st sea-level change projections, including uncertainty bounds. It is widely accepted that land-ice behaves like a very viscous, shear-thinning, non-Newtonian fluid, similar to lava flow. Typically, ice sheets are modeled using a quasi-static model in which a steady momentum-balance system for the ice velocities is coupled to dynamic equations for the ice thickness and temperature. The Albany/FELIX dycore is based on the so-called “First-Order Stokes” equations for the ice momentum balance [4], an attractive alternative to the more expensive “Full Stokes” model because of its reduced computational cost. Following an overview of our land-ice model and project, I will describe some of the algorithms and software we have developed as a part of this project that have contributed to our dycore’s robustness and scalability. These include: robust automatic-differentiation-based nonlinear solvers, scalable algebraic-multigrid-based iterative linear solvers [5], adaptive mesh refinement capabilities, and stable semi-implicit First-Order Stokes-thickness coupling methods. I will also discuss some of the advanced analysis capabilities in Albany/FELIX, namely a large-scale inversion approach we have developed for obtaining optimal ice initial conditions [6], our workflow towards quantifying uncertainties in land-ice models, and performance-portability of the Albany/FELIX code to new and emerging architectures using the Kokkos library [7]. I will show results which demonstrate that the Albany/FELIX dycore is scalable, fast and robust for production-scale land-ice problems on state-of-the-art HPC machines. I will also discuss results from a recent validation study in which Albany/FELIX was used to simulate the Greenland ice sheet during the period 1991-2013 with realistic climate forcing, and the simulation data were compared with observational data collected by NASA satellites [8].
This work was done in collaboration with Irina Demeshko, Mike Eldred, Matt Hoffman, John Jakeman, Mauro Perego, Steve Price, Andy Salinger, Ray Tuminaro and Jerry Watkins.
Dr. Tezaur is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet her please email mcteja@umich.edu
[1] I. Velicogna. Increasing rates of ice mass loss from the Greenland and Antarctic ice sheets revealed by GRACE. Geophysical Research Letters, 36 (19) L19503, 2009.
[2] S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. Averyt, M. Tignor, H. Miller. Climate change 2007: The physical science basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge Univ. Press, Cambridge, UK, 2007.
[3] I. Tezaur, M. Perego, A. Salinger, R. Tuminaro, S. Price. Albany/FELIX: A Parallel, Scalable and Robust Finite Element Higher-Order Stokes Ice Sheet Solver Built for Advanced Analysis, Geosci. Model Develop. 8 (2015) 1-24.
[4] J.K. Dukowicz, S.F. Price, W. Lipscomb. Consistent approximations and boundary conditions for ice-sheet dynamics from a principle of least action. J. Glaciol., 56 (197) (2010) 480-496.
[5] R. Tuminaro, M. Perego, I. Tezaur, A. Salinger, S. Price. A matrix dependent/algebraic multigrid approach for extruded meshes with applications to ice sheet modeling, SIAM J. Sci. Comput. 38 (5) (2016) C504-C532.
[6] M. Perego, S. Price, G. Stadler. Optimal initial conditions for coupling ice sheet models to earth system models, J. Geophys. Res., 119 (2014) 1894-1917.
[7] H.C. Edwards, C.R. Trott, D. Sunderland. Kokkos: Enabling manycore performance portability through polymorphic memory access patterns. J. Par. and Distr. Comput., 74 (12) 3202–3216, 2014.
[8] S. Price, M. Hoffman, J. Bonin, T. Neumann, I. Howat, J. Guerber, I. Tezaur, J. Saba, J. Lanaerts, D. Chambers, W. Lipscomb, M. Perego, A. Salinger, R. Tuminaro. An ice sheet model validation framework for the Greenland ice sheet, Geosci. Model Dev. 10 (2017) 255-270

MICDE Seminar: Panos Papadopoulos, Department of Mechanical Engineering, University of California, Berkeley
2017-10-19 15:30 - 16:30 | Johnson Rooms, Lurie Engineering Center, 3rd Floor LEC 3213ABC, 1221 Beal Ave., Ann Arbor, MI, United States
Bio: Panos Papadopoulos is a Professor of Mechanical Engineering at the University of California, Berkeley, and director of the Computational Solid Mechanics Laboratory. After obtaining his Diploma in Civil Engineering from the Aristotle University, Greece, he moved to California to pursue his graduate studies. He obtained his M. Sc. and Ph.D. in Civil Engineering from UC Berkeley. His research involves experimental, analytical and computational studies of several mechanics systems. Prof. Papadopoulus develops and applied the finite element method to problems in biomechanics, dynamics of pseudo-rigid bodies, mechanics of continues media, plasticity, materials science and contact mechanics.
Multiscale Modeling in Continuum Mechanics: A connection to the Irving-Kirkwood procedure
This talk describes a method for extending the classical Irving-Kirkwood procedure used in statistical mechanics for extracting local fluxes to the problem of continuum-on-continuum multiscale modeling. Expressions for stress and heat flux derived here are contrasted to those obtained using the standard Hill-Mandel approach. The polar nature of the macroscopic solid and the role of multiscale invariance are also addressed in the context of this method. Applications are explored within the finite element-based homogenization of solids.
Prof. Papadopoulos is being hosted by Prof. Garikipati (Mechanical Engineering). If you would like to meet with him please send an email to mcteja@umich.edu
