MICDE Seminar: Douglas Spearot, Professor of Mechanical & Aerospace Engineering, University of Florida
2022-04-05 15:00 - 16:00 (America/Detroit) | 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

Software Development For Research: Team-Based Programming Management
2022-04-13 10:00 - 12:00 | Your Desktop
Software development and computer programming is increasingly a major part of scientific research. When small or large teams are involved in coding the same project, it’s a good idea to adopt team-based practices, such as “pair programming” or “code review” meetings, to ensure quality is maintained in the code. This hands-on workshop will provide examples of these topics in action, and attendees will leave prepared to use these skills in their own work groups.
This is part of a series of workshops focused on both technical and soft skills regarding software engineering from a research perspective.
Please register at least 48 hours in advance.

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

SiTime Research Partnership and Career Event
2022-05-10 15:00 - 16:00 | Johnson Rooms, Lurie Engineering Center, 3rd Floor, 1221 Beal Ave., Ann Arbor, MI, United States
This event will also be broadcasted via Zoom. Please register.
Refreshments will be served. Please REGISTER by May 6, 2022, specially if you are planning to attend in person. You’ll need to use your U-M credentials.
SiTime, a market leader in MEMS timing, will present an overview of its business, products and some of the tough computational science and FEA related problems it is working to solve in its MEMS resonator (timing reference) design. SiTime has a world leading computational science and FEA based design group and is looking for partnerships with research groups at the University of Michigan, and great talents (graduate students and post docs hire) to help accelerate its innovation as it revolutionizes the timing industry with groundbreaking solutions.
Resumes of graduate students and post docs are welcome for internship and job opportunities!

SiTime Corporation, a market leader in MEMS timing, offers MEMS-based silicon timing system solutions. SiTime’s configurable solutions offer a rich feature set that enables customers to differentiate their products with high performance, small size, low power, and high reliability. With over 1.5 billion devices shipped to date, SiTime is changing the timing industry.

University of Michigan faculty and students interested in finite element methods, microelectromechanical systems, shape optimization, computational geometry, continuum mechanics, non-linear behavior, multiparametric non-convex constrained optimization or materials science are encouraged to attend.
This is a hybrid event and will be held in-person and broadcast online via Zoom. Please register by May 6, 2022

Data Scraping With The Twitter Decahose At The University of Michigan
2022-05-13 13:00 - 15:00 | Your Desktop
Social media platforms are a rich source for data. The University of Michigan collects data with the Twitter Decahose, maintaining an archive of 10% of tweets made from the past decade. This collection is maintained in collaboration by MIDAS, CSCAR and ARC.
This workshop covers what the Twitter Decahose is, the process to obtain access, and details on the data format and metadata included, with live examples to process a sample into a filtered set using Python and PySpark.
More information on research datasets from MIDAS can be found at: https://midas.umich.edu/research-datasets/
Please register at least 48 hours in advance.

Midwest Numerical Analysis Day 2022
2022-05-20 13:00 - 2022-05-21 17:30 | East Hall, 530 Church St, Ann Arbor, MI, 48109, United States
The Midwest Numerical Analysis Day (MWNADay) is a forum for researchers at all stages of their careers, mainly from the Midwest, to exchange ideas in numerical analysis, scientific computing and related application areas.
This year it will take place in person and on line at the University of Michigan, Ann Arbor on May 20 & May 21.
Participants are invited to give a contributed talk or present a poster. Participation of graduate students and postdocs is encouraged. Partial travel support is available.
For more information and to register, please visit the event’s site.

MWNAD 2022 is sponsored by the University of Michigan Department of Mathematics, the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM) and the Michigan Institute for Computational Discovery and Engineering (MICDE).
Email questions about this year’s event to MWNADadmin@umich.edu.

2022 SIAM Student Mini-Symposium in Applied Mathematics
2022-05-27 10:00 - 16:00 | 1372 East Hall, 530 Church St., Ann Arbor, MI, 48109, United States
The SIAM student chapter at the University of Michigan is hosting its 3rd Annual Student Mini-symposium in Applied Mathematics. This event will allow students from different disciplines in the area to see what is being done in the field and promote interest in applied mathematics in general.
This event is open to all graduate students in the University of Michigan. Students are invited to submit abstracts for short presentations. Deadline to submit an abstract is May 13, 2022. To register please fill out this form by May 15, 2022.
Free food and drinks will be provided to all registered attendees!
Event’s Website | Event Poster

Questions? Please email Christiana Mavroyiakoumou at chrismav@umich.edu or any of the organizer listed on the event’s website.

A Beginner's Guide To Web Scraping With Python
2022-05-27 13:00 - 15:00 | MI
There’s a ton of data on the web! You can collect it by using “web scraping!” But what is web scraping? And how do you do it?
This workshop is a beginner’s guide to web scraping and how to get started using Python and the BeautifulSoup library. Attendees will leave with a better understanding of what web scraping is, and some strategies to implement solutions for it. To participate, users are expected to be able to run Python on their local computer or through a remote service from campus. A basic understanding of Python and computer programming is strongly encouraged before attending this workshop. A basic understanding of HTML programming is recommended but not mandatory.
Please register at least 48 hours in advance.

2022 MICDE Annual Symposium
2022-05-31 08:00 - 2022-10-21 17:00 | MI
Save the Date!
After a 2-year wait, the 2022 MICDE Annual Symposium will take place on Fri., October 21, 2022.
The symposium will feature prominent scientists that are driving

Features speakers will be posted soon.

Introduction to Julia for Statisticians and Data Scientists - Day 1
2022-08-15 09:00 - 11:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States
This three-part workshop will give an overview of the Julia language for conducting data analyses and managing data. The workshop assumes no prior experience with Julia, but participants should have prior programming experience with a language such as Python, R, or Matlab. The format of the workshop is lecture/discussion but participants may bring a laptop if they wish to have a hands-on experience. The instructor will be using Julia on greatlakes for demonstration so it is advisable (but not essential) for participants to have a greatlakes account.
Day 1: Introduction to the language and tools, managing data with dataframes, producing simple graphs
Day 2: Basic regression analysis using generalized linear models
Day 3: Intermediate regression analyses using GEE, mixed models, and survival analysis

Introduction to Julia for Statisticians and Data Scientists Day #2
2022-08-16 09:00 - 11:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States

Introduction to Julia for Statisticians and Data Scientists Day #3
2022-08-18 09:00 - 11:00 | Rackham Building, Earl Lewis Room, 3rd Floor East, 915 E. Washington St., Ann Arbor, MI, 48109, United States

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.

Introduction to Stata: The Basics of Stata
2022-10-03 13:00 - 14:30 | Your Desktop
This is a series of workshops designed to introduce participants to the Stata software. No prior experience with Stata is required. The sections are:
Section 1: The Basics of Stata – Interacting with Stata and working with data sets. (10/03, 1:00 – 2:30)
Section 2: Data Management – The basics of maintaining and exploring a data set. (10/05, 1:00 – 2:30)
Section 3: Data Manipulation – Creating and modifying variables and other ways of manipulating your data. (10/07, 1:00 – 2:30)
You do not need to attend all sessions; however, the sessions build on each other and it will be assumed you are familiar with the material in earlier sessions. The workshop materials can be found at https://cscar.github.io/workshop-stata-intro/ for review.
It is strongly encouraged that you have Stata available on your local computer, though not required. You may access Stata through midesktop (https://midesktop.umich.edu/) if needed.

Introduction to Stata: Data Management
2022-10-05 13:00 - 14:30 | Your Desktop

Introduction to Stata: Data Manipulation
2022-10-07 13:00 - 14:30 | Your Desktop

MICDE & MIDAS Graduate Programs Info Session North Campus
2022-10-26 13:30 - 14:30 | Johnson Rooms, Lurie Engineering Center, 3rd Floor, 1221 Beal Ave., Ann Arbor, MI, United States
Join the MICDE and MIDAS teams for a 1-hour information session to learn more about our computational and data science graduate program offerings, including: the Ph.D. in Scientific Computing, the Graduate Certificate in Computational Discovery & Engineering, the Graduate Certificate in Computational Neuroscience, and the Graduate Certificate in Data Science.
After a short presentation, each program’s faculty director and/or staff manager will be present to answer questions.

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 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 multiscale 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 & MIDAS Graduate Programs Info Session Central Campus
2022-11-02 13:30 - 14:30 | 340 West Hall, 1085 South University Ave., Ann Arbor, MI, 48109, United States

PhD Seminar: Srihari Sundar and Vishwas Goel
2022-11-03 16:00 - 17:00 | Weiser Hall, 6th Floor, 619, 500 Church Street, Ann Arbor, MI, 48109, United States
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speakers:
Srihari Sundar, PhD Candidate, Aerospace Engineering and Scientific Computing
Hari’s research interests include decarbonization of the power sector, climate impacts, computational modeling, and sustainable transformation. His current research in the center for sustainable systems is focused on predicting changes in the energy system — meteorology interaction with a transition to widespread renewable energy generation. He aspires to use this to inform long term planning of reliable power systems under a changing climate while ensuring a just transition.
LinkedIn Twitter
Meteorological Drivers of Resource Adequacy Failures During the Transition to a Decarbonized Power System
Increasing meteorological extremes and renewable penetrations could challenge resource adequacy (RA) in the electric power system, as demonstrated by recent blackouts in California and Texas. We quantify meteorological drivers of RA in the Western U.S. power system, and examine how these drivers change with increasing renewable penetrations. Our analysis integrates an optimization-based capacity expansion model, stochastic RA model, and neural-network-based self-organizing maps. We find that RA failures are driven by high pressure circulation patterns which produce positive surface temperature anomalies and negative solar radiation and wind speed anomalies. Further, with increasing renewable penetration we find that the probability of failure attributed to patterns associated with heat waves over the region increases.

Vishwas Goel, PhD Candidate, Materials Science and Engineering and Scientific Computing
Vishwas is a Ph.D. candidate in the Department of Materials Science and Engineering. His research is primarily focused on simulating electrochemical phenomena on multiple scales.
LinkedIn
Simulating microgalvanic corrosion in Mg alloys using PRISMS-PF
Magnesium and its alloys are the lightest structural metallic materials known, and therefore, hold vast potential for reducing the weight for various transportation modes such as airplanes, cars, buses, etc. Although the alloying of Mg with elements such as Al, Mn, and rare earth (RE) elements is known to improve the mechanical properties of Mg, the process is often detrimental to the corrosion performance of Mg. This increase in the corrosion rate occurs because of the micro-galvanic couple that forms between the Mg-rich phase, which acts as an anode, and the alloying-element-rich phase, which acts as a cathode.
Using both experiments and modeling, it has been reported that the rate of micro-galvanic corrosion in the Mg-alloys depends on the alloying element and microstructure. However, a deeper understanding is required for quantifying the effect of microstructure characteristics such as the fraction of the two phases, spacing between the two phases, the geometry of the two phases, etc., on the corrosion rate. This understanding is crucial for designing Mg-alloys with optimal mechanical properties and high corrosion resistance.
To bridge this gap in our understanding, we perform the continuum-scale phase-field modeling of different microstructures observed in Mg-alloys. Furthermore, we complement the modeling work with theoretical analysis, where we develop analytical relations for studying the effect of various material and microstructural parameters on the characteristic corrosion length scale. The results from both these efforts will be summarized in our presentation.

This event is part of MICDE’s Fall 2022 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.
Questions? Email MICDE-events@umich.edu

Listening Session with ARC Director Brock Palen
2022-11-10 11:00 - 12:00 | MI
You’re invited to hear from Brock Palen about the next generation of Advanced Research Computing (ARC) services, and to provide feedback about whether these services will meet your needs. You are also welcome to share what is currently working for you, and what is not.
This is an open, drop-in office hour, and all are welcome.
Zoom Meeting information:
umich.zoom.us/j/98482954458
ID: 98482954458
Join by phone
(US) +1 669-900-6833
(CA) +1 204-272-7920
Join using SIP
98482954458@zoomcrc.com
Joining instructions (https://applications.zoom.us/addon/invitation/detail?meetingUuid=MqlvGqsVQ3%2BkySq0nx5V4Q%3D%3D&[REDACTED]v=1)
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Meeting host: brockp@umich.edu
Join Zoom Meeting:
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ARC is a division of Information and Technology Services (ITS).

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

Listening Session with ARC Director Brock Palen
2022-11-14 10:00 - 11:00 | MI
You’re invited to hear from Brock Palen about the next generation of Advanced Research Computing (ARC) services, and to provide feedback about whether these services will meet your needs. You are also welcome to share what is currently working for you, and what is not.
This is an open, drop-in office hour, and all are welcome.
ARC is a division of Information and Technology Services (ITS).
Join Zoom Meeting
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Meeting host: brockp@umich.edu
Join Zoom Meeting:
https://umich.zoom.us/j/91451008889

Listening Session with ARC Director Brock Palen
2022-11-16 11:30 - 12:30 | MI
You’re invited to hear from Brock Palen about the next generation of Advanced Research Computing (ARC) services, and to provide feedback about whether these services will meet your needs. You are also welcome to share what is currently working for you, and what is not.
This is an open, drop-in office hour, and all are welcome.
ARC is a division of Information and Technology Services (ITS).
Zoom Meeting information:
umich.zoom.us/j/92727272942
ID: 92727272942
Join by phone
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Joining notes
Meeting host: brockp@umich.edu
Join Zoom Meeting:
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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.

PhD Seminar: Khoi Dang
2022-11-17 16:00 - 16:30 | Weiser Hall, 6th Floor, 619, 500 Church Street, Ann Arbor, MI, 48109, United States
The Ph.D. in Scientific Computing program is intended for students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their doctoral studies. This seminar series showcases the breadth of research covered by the program.
Featured Speaker:
Khoi Dang, PhD Candidate, Chemistry and Scientific Computing
Khoi is a 5th year graduate student in the Chemistry Department currently developing electronic structure theory methods in the Zimmerman Group.
Parallel Heat-bath Configuration Interaction
The heat-bath configuration interaction (HBCI) method is a deterministic wave function method that approaches the full CI limit at greatly reduced cost. HBCI consists of two parts: the generation of a variational wave function, followed by a perturbative correction. This work introduces a parallel implementation that is highly scalable and overcomes the memory bottleneck of perturbation theory. The implementation demonstrates 83% parallel efficiency for the perturbative step on 32 nodes.

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

Introduction to R: The Basics of R
2022-11-30 13:00 - 15:30 | MI
This is a series of workshops to introduce the R programming language, as well as using R for data management, manipulation and visualization. No prior experience with R is required. These workshops are divided into the following three sessions:

1. The Basics of R — Basic concepts and commands of the R programming language.
2. Data Management and Manipulation — Exploring, organizing and manipulating data using dplyr syntax.
3. Data Visualization – Create informative graphics with improved quality and aesthetics using ggplot2.

It is not required to attend all three sessions. However, sessions are built on each other and it is assumed that participants are familiar with materials presented in earlier sessions.
