Jin Zhao, Associate Professor, Capital Normal University

Jie Xu, Associate Professor

Mechanism-Data Fusion Method for Modeling Heat Conduction

9:30-10:30, August 7 (Monday), Z311

In this talk, we present a mechanism-data fusion method (MDFM) for modeling heat conduction with two-dissipative variables. This method enjoys mathematical rigor from physical laws, adaptability from machine learning, and solvability from conventional numerical methods. Specifically, we use the conservation-dissipation formalism (CDF) to derive a system of first-order hyperbolic partial differential equations (PDEs) for heat conduction, which naturally obeys the first and second laws of thermodynamics. Next, we train the unknown functions in this PDE system with deep neural networks. Moreover, we propose a novel method, the Inner-Step operation (ISO), to narrow the gap from the discrete form to the continuous system. We conduct lots of numerical experiments which show that the proposed model can well predict the heat conduction in diffusive, hydrodynamic and ballistic regimes. This is a joint work with Leheng Chen and Chuang Zhang.

报告人简介: 赵进博士，2014、2017年于西安交通大学分别获学士、硕士学位，2020年于北京计算科学研究中心获博士学位，2020-2022年在北京大学数学科学学院从事博士后研究，2022年至今在首都师范大学交叉科学研究院任职。