Past Event: Oden Institute Seminar

Physics-constrained data-driven methods of accurately accelerating simulations and their applications

Youngsoo Choi, Computational scientist, Lawrence Livermore National Laboratory

Abstract

A reduced order model is built to accurately accelerate computationally expensive physical simulations, which is useful in multi-query problems, such as inverse problem, uncertainty quantification, design optimization, and optimal control. In this talk, two types of data-driven model order reduction techniques will be discussed, i.e., the black-box approach that incorporates only data and the physics-constrained approach that incorporates the first principle as well as data within the reduced order models. The advantages and disadvantages of each method will be discussed. Furthermore, several recent developments at LLNL of data-driven physics-constrained reduced order modeling techniques will be introduced in the context of various physical simulations. For example, a hyper-reduced time-windowing reduced order model overcomes the difficulty of advection-dominated shock propagation phenomenon, achieving a speed-up of O(20~100) with a relative error much less than 1% for Lagrangian hydrodynamics problems. The space–time reduced order model accelerates a large-scale particle Boltzmann transport simulation by a factor of 2,900 with a relative error less than 1%. The nonlinear manifold reduced order model shows perfect combination between machine learning and the existing numerical discretization methods, such as finite element and finite difference. Finally, successful application of these reduced order models in design optimization problems will be presented.

Biography

Youngsoo is a computational scientist in Center for Applied Scientific Computing division at LLNL. His research focus lies on developing efficient data-driven methods of accelerating various physical simulations to be used in time sensitive decision-making applications. He is currently leading data-driven surrogate model development team at LLNL. He has earned his undergraduate degree for Civil and Environmental Engineering from Cornell University and his PhD degree for Computational and Mathematical Engineering from Stanford University. He was a postdoc in Sandia National Laboratory and Stanford University prior to joining LLNL in 2017.   https://people.llnl.gov/choi15