Physics-informed neural networks for solving differential equations

Prerequisite

Knowledges on mathematical physics equations, deep neural networks and the Python language.

Abstract

Differential equations can describe various natural and social phenomena. The physics-informed neural networks (PINNs), as a deep learning framework, is a powerful and effective way in solving forward and inverse problems involving differential equations. The course will review the publications of the recent years on PINNs, including explanation of the principle, numerical examples and codes of vanilla PINN and the various improved methods. At the same time, audiences studying PINNs are encouraged to share their research experience and achievements.

Lecturer Intro.

熊繁升，现任北京雁栖湖应用数学研究院助理研究员，曾任北京应用物理与计算数学研究所所聘博士后。先后毕业于中国地质大学(北京)、清华大学，美国耶鲁大学联合培养博士。研究兴趣主要集中于基于机器学习算法(DNN、PINN、DeepONet等)求解微分方程模型正/反问题及其在地球物理波传播问题中的应用，相关成果发表在JGR Solid Earth、GJI、Geophysics等期刊上。