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Dieter Fox, University of Washington
Dieter Fox is Senior Director of Robotics Research at NVIDIA and Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where he heads the UW Robotics and State Estimation Lab. Dieter obtained his Ph.D. from the University of Bonn, Germany. His research is in robotics and artificial intelligence, with a focus on state estimation and perception applied to problems such as mapping, object detection and tracking, manipulation, and activity recognition. He has published more than 200 technical papers and is the co-author of the textbook “Probabilistic Robotics”.
He is a Fellow of the IEEE, AAAI, and ACM, and recipient of the 2020 Pioneer in Robotics and Automation Award. Dieter also received several best paper awards at major robotics, AI, and computer vision conferences. He was an editor of the IEEE Transactions on Robotics, program co-chair of the 2008 AAAI Conference on Artificial Intelligence, and program chair of the 2013 Robotics: Science and Systems conference.
Pascal Van Hentenryck, Georgia Tech
Pascal Van Hentenryck is the A. Russell Chandler III Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He is also the executive director of the NSF AI Institute for Advances in Optimization. Several of his optimization systems have been in commercial use for more than 20 years. He is an INFORMS Fellow and a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the recipient of two honorary doctoral degrees, and several teaching excellence awards at Brown University and Georgia Tech.
Dr. Hentenryck’s current research focuses on machine learning, optimization, and privacy with applications in energy systems, mobility, and supply chains. He explores methodologies that includes large-scale optimization and machine learning and applies them in challenging applications in energy, mobility, supply chains and logistics, privacy, and resilience.
Anna Stefanopoulou, University of Michigan
Professor Anna Stefanopoulou, the William Clay Ford Professor of Technology and Professor of Mechanical Engineering at the University of Michigan is leading an interdisciplinary group and advised more than 40 PhD students and 10 postdoctoral fellows in powertrain control especially state estimation, model-based prediction, data-driven fault-detection and automation of dynamic and multivariable problems in electrochemical and thermomechanical processes for battery management, fuel cells, and engines.
She was the Director of the UM Energy Institute (2018-2020) and the Director of the Automotive Research Center, a multi-university U.S. Army Center of Excellence in Modeling and Simulation of Ground Vehicles (2009-2018). Before Michigan, she was an assistant professor at the University of California, Santa Barbara, a visiting professor at ETH, Zurich, and a technical specialist at Ford.
She is a Fellow of the ASME (08), IEEE (09), and SAE (18) and has been recognized in her field with multiple awards. She was an elected member of the Executive Committee of the ASME Dynamics Systems and Control Division and the Board of Governors of the IEEE Control Systems Society, the Founding Chair of the ASME DSCD Energy Systems Technical Committee.
Decoding the electrode swelling for advanced battery diagnostics and life prognostics
The battery management system (BMS) relies on accurate prediction and management of complex electrochemical, thermal and mechanical phenomena. This raises the question of model and parameter accuracy that may allow the estimation of remaining useful life. To this end, novel sensing and diagnostic techniques based on “aging wrinkles” from battery cell swelling due to intercalation, particle fracture, rust-like film growth, and gas evolution will be presented. We will conclude by highlighting the ultimate BMS safety task, namely leveraging the battery swelling measurements for estimating the onset of venting and thermal runaway, and consequently, managing the risk of explosions and fires from failing batteries.
Somnath Ghosh, John Hopkins University
Professor Somnath Ghosh is the Michael G. Callas endowed Chair Professor in the Department of Civil & Systems Engineering and Professor of Mechanical Engineering and Materials Science & Engineering at Johns Hopkins University. He is the founding director of the JHU Center for Integrated Structure-Materials Modeling and Simulation (CISMMS) and was the director/PI of the Air Force Center of Excellence in Integrated Materials Modeling (CEIMM). His research focuses on multi-scale structure-materials analysis and simulations, multi-physics modeling and simulation of multi-functional materials, materials characterization, process modeling, and emerging fields like Integrated Computational Materials Engineering (ICME). He has conducted pioneering research to advance the field of integrated computational structure-materials modeling into new areas of importance and challenges.
His research interests are Computational Mechanics with a focus on materials modeling, multi-scale structure-materials analysis and simulations, multi-physics modeling and simulation of multi-functional materials, materials characterization, and process modeling.
Data-Driven Bayesian Model for Predicting Fatigue Crack Nucleation in Polycrystalline Ni-based Superalloys
This talk will discuss a Bayesian inference-based probabilistic crack nucleation model for the Ni-based superalloy Ren\’e 88DT under fatigue loading. A data-driven, machine learning approach is developed to identify the underlying mechanisms driving crack nucleation. An experimental set of fatigue-loaded microstructures is characterized near crack nucleation sites using scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD) images for correlating the grain morphology and crystallography to the spatial location of crack nucleation sites. A concurrent multiscale model that embeds experimentally acquired polycrystalline microstructural RVEs in a homogenized material, is developed for fatigue simulations. The RVE domain is modeled by a crystal plasticity FEM, while a self-consistent homogenized anisotropic plasticity model is used for the exterior domain. The simulations create a database of state variables to be used in the model development. A Bayesian classification method is introduced to optimally select the most informative state variable predictors of crack nucleation and constructs a near-Pareto frontier of models with varying complexity. From this principal set of state variables, a simple scalar crack nucleation indicator is formulated, which encompasses relevant components derived from the main discriminators. This Bayesian inference approach allows the micromechanical state variables responsible for crack nucleation to evolve naturally from the existing data. The resulting model predicts the probability of nucleating a crack at a microstructural location, given the mechanical state of the material.
Thank you to our event sponsors the Grainger Institute for Engineering, Wisconsin Applied Computing Center, Mandli Communication, Brian Eckrose Foundation, Hexagon AB, and Epic
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List of 2021 Participating Organizations:
3M, Aalto University, Aarhus University, Adobe Research, AFI Data Science Institute, Agrointelli, Alion Science & Technology, Altair Engineering, Amazon, Amcor, American Family Insurance, ANSYS, Aramco Services Company, Arizona State University, BAE Systems, BITS Pilani Hyderabad-India, Blue River Technology, Boston University, Brigham Young University, Caltech, Carnegie Mellon University, Caterpillar, Chungnam National University, Clemson University, Cleveland Clinic, CM Labs Simulations, Colorado State University, Columbia University, CSIR-South Africa, Donaldson, Dow Chemical, DSIM TECH, Duke University, Dynamic Dimension Technologies, Eaton Corporation, Energid, Entos, ESR-New Zealand, ExxonMobil Research and Engineering, Fero Labs-BASF, FEUP, General Motors Corp, Globus Labs, Google, Harley-Davidson Motor Company, Harvard University, Heidelberg University, Hexagon, Hong Kong University of Science & Technology, Hyper Innovation, Indian Institute of Technology Ropar, Indiana University, Institute of Space Technology-Pakistan, Intact, Intel, International Society for Terrain-Vehicle Systems, Iowa State University, ISAE-SUPAERO, Istanbul Medipol University, Jet Propulsion Lab, JLG Industries, John Deere, Johns Hopkins University, JSlat Consulting, Karlsruhe Institute for Technology, Lawrence Livermore National Laboratory, Lehigh University, Leidos Inc., Louisiana State University, Mandli Communications, Manipal Institute of Technology, Massachusetts Institute of Technology, MathWorks, McGill University, McMaster University, Michigan State University, Michigan Technological University, Mississippi State University, Mitsubishi Electric Research Labs, Morgridge Institute for Research, Morpheus Space, MSC Software, Nanyang Technological University, NASA, National Advanced Driving Simulator – University of Iowa, National University of Singapore, Nevada Automotive Test Center, New Jersey Institute of Technology, Northwestern University, NTU Singapore, Obafemi Awolowo University-Nigeria, Ohio University, Oklahoma State University, Open Robotics, Oshkosh Corporation, Pacific Northwest National Laboratory, Pratt & Miller, Princeton University, Purdue University, Raytheon Technologies Research Center, RightHook, Romax Technology, Software Cradle, SRI International, Stanford University, Stevens Institute of Technology, SYAM, Team Penske, Tesla, Texas A&M University, Texas Tech University, U.S. Army Corps of Engineers, U.S. Army ERDC CRREL, U.S. Army ERDC, U.S. Army GVSC, U.S. Army Research Office, U.S. Naval Research Laboratory, UMass-Amherst, UNAM, Universidad Austral de Chile, Universidade de Vigo, University of British Columbia, University of Alabama, University of Alabama-Tuscaloosa, University of British Columbia, University of California Berkeley, University of California Santa Barbara, University of Chicago, University of Delaware, University of Florida, University of Guanajuato, University of Helsinki, University of Illinois at Urbana-Champaign, University of Iowa, University of Maryland, University of Michigan, University of Minnesota, University of Notre Dame, University of Pittsburgh, University of Pretoria-South Africa, University of Salerno, University of Sci.&Tech. Beijing, University of Sheffield, University of Texas at Austin, University of Ulsan, University of Utah, University of Virginia, University of Virginia School of Medicine, University of Waterloo, University of Wisconsin-Madison, University of Wisconsin-Oshkosh, University of Wisconsin-Platteville, University of Wisconsin-Stout, UPM, USDOT Federal Highway Administration, Walt Disney Animation Studios, Wisconsin Alumni Research Foundation, Wyss Institute, Yale University