Title:  Physics-constrained data-driven methods for accurately accelerating simulations

Abstract:  A data-driven model can be built to accurately accelerate computationally expensive physical simulations, which is essential 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. The advantages and disadvantages of each method will be discussed. Several recent developments of generalizable and robust data-driven physics-constrained reduced order models will be demonstrated for various physical simulations as well. 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, such as 3D Sedov blast problem, 3D triple point problem, 3D Taylor Green vortex problem, 2D Gresho vortex problem, and 2D Rayleigh Taylor instability problem. The nonlinear manifold reduced order model also overcomes the challenges posed by the problems with Kolmogorov’s width decaying slowly by representing the solution field with a compact neural network decoder, i.e., nonlinear manifold. The space time reduced order model accelerates a large-scale particle Boltzmann transport simulation by a factor of 2,700 with a relative error less than 1%. Furthermore, successful application of these reduced order models for mate-material lattice structure design optimization problems will be presented. Finally, the library for reduced order modeling tool, i.e., libROM (https://www.librom.net), and its webpage and several YouTube tutorial videos will be introduced.

Speaker:  Youngsoo Choi, Research Scientist, Lawrence Livermore National Laboratory

Bio: Youngsoo is a research scientist in Center for Applied Scientific Computing at LLNL. His research focuses on developing efficient reduced order models for various physical simulations for time-sensitive decision-making multi-query problems, such as inverse problems, design optimization, and uncertainty quantification. His expertise includes various scientific computing disciplines. He has also developed the component-wise reduced order model optimization algorithm, which enables fast and accurate computational modeling tool for lattice-structure design. He is currently leading data-driven surrogate modeling development team for various physical simulations, with whom he developed an open source code, libROM. 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 at Sandia National Laboratories and Stanford University prior to joining LLNL in 2017.

Presenter Website: https://people.llnl.gov/choi15
Department Link: https://computing.llnl.gov/casc

Host:  Jose Castillo (CSRC) and the Sustainable Horizons Institute CRLC Virtual Seminar Series