DDPS Talk date:
· Speaker: Boris Kramer (UC San Diego, https://kramer.ucsd.edu/)
· Description: Lagrangian and Hamiltonian mechanics are foundational modeling approaches in diverse areas such as structural mechanics, aerospace engineering, wave propagation, biomedical engineering, high-energy physics, quantum mechanics, solid-state physics, and soft robotics. These systems exhibit physically interpretable quantities such as momentum, energy, or vorticity; the behavior of these quantities in numerical simulation provides an important measure of accuracy of the model. For the data-driven modeling and simulation of such high-dimensional and structured complex dynamical systems, it is essential to first reduce the dimensionality to manageable (reduced) dimensions and then to incorporate the Lagrangian and Hamiltonian structure into the learning framework. This talk gives an overview of a few recently developed approaches for learning structure-preserving reduced-order models, with applications to structural mechanics, soft robotics, and wave propagation. With much fewer training data than non-structured learning methods require, the Lagrangian and Hamiltonian learned reduced models provide stability and robustness while showing exceptional long-term predictive accuracy.
· Bio: Boris Kramer is an Assistant Professor in Mechanical and Aerospace Engineering at the University of California San Diego. Prior to joining UC San Diego, he spent four years as a Postdoctoral Associate in the department of Aeronautics and Astronautics and the Aerospace Computational Design Lab (ACDL) at the Massachusetts Institute of Technology (MIT). He received his M.Sc. (2011) and Ph.D. (2015) in Mathematics from Virginia Tech. Prior to that, he studied Mathematics in Technology and Mechanical Engineering at the University of Karlsruhe (now KIT), Germany. He is a member of the Society for Industrial and Applied Mathematics (SIAM), and a Senior Member of AIAA where he also served on the Multidisciplinary Design Optimization and is active in the Nondeterministic Approaches Technical Committees. He is a 2022 NSF CAREER Awardee and won a Department of Defense Newton Award in 2020. His research is funded by the Office of Naval Research (ONR), the Defense Advanced Research Projects Agency (DARPA) and the National Science Foundation. His research interests are to develop computational methods and numerical analysis for control, optimization, design and uncertainty quantification of complex and large-scale systems.

DDPS webinar: https://www.librom.net/ddps.html
💻 LLNL News: https://www.llnl.gov/news
📲 Instagram:   / livermore_lab  
🤳 Facebook:   / livermore.lab  
🐤 Twitter:   / livermore_lab  

About LLNL: Lawrence Livermore National Laboratory has a mission of strengthening the United States’ security through development and application of world-class science and technology to: 1) enhance the nation’s defense, 2) reduce the global threat from terrorism and weapons of mass destruction, and 3) respond with vision, quality, integrity and technical excellence to scientific issues of national importance. Learn more about LLNL: https://www.llnl.gov/.

IM release number is: LLNL-VIDEO-859855