2024年02月23日 星期五 登录 EN

学术活动
Structure-preserving machine learning moment closures for the radiative transfer equation
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报告人:
Juntao Huang, Assistant Professor, Texas Tech University
邀请人:
Yong Liu, Associate Professor
题目:
Structure-preserving machine learning moment closures for the radiative transfer equation
时间地点:
9:00-10:00 February 8( Wednesday) ,Tencent Meeting ID: 131-394-052
摘要:
In this talk, we present our work on structure-preserving machine learning (ML) moment closure models for the radiative transfer equation. Most of the existing ML closure models are not able to guarantee the stability, which directly causes blow up in the long-time simulations. In our work, with carefully designed neural network architectures, the ML closure model can guarantee the provable stability (or hyperbolicity). Moreover, other mathematical properties, such as physical characteristic speeds, are also discussed. Extensive benchmark tests show the good accuracy, long-time stability, and good generalizability of our ML closure model. This is a joint work with Yingda Cheng, Andrew Christlieb, Luke Roberts and Wen-An Yong. 
 
报告人简介:Dr. Juntao Huang is an Assistant Professor at Texas Tech University. He obtained the PhD degree in Applied Math in 2018 and the bachelor degree in 2013 from Tsinghua University. Prior to joining Texas Tech University in 2022, he worked as a visiting assistant professor at Michigan State University. His research interests involve the design and analysis of numerical methods for PDEs and using machine learning to assist traditional scientific computing tasks. His recent work includes structure-preserving machine learning moment closures for kinetic models and adaptive sparse grid discontinuous Galerkin (DG) methods for high-dimensional PDEs.