Solving Partial Differential Equations with Data Driven Machine Learning Methods

Prerequisite

Basic knowledge on numerical methods for partial differential equations and machine learning methods

Abstract

This course reviews the publications of the recent years on using machine learning methods to solve partial differential equations, such as Physics Informed Neural Network (PINN). The course will cover the materials on forward method, inverse method, reduced order modeling, and the assimilation of observational data to the scientific principles.

Lecturer Intro.

Dr. Zhang Xiaoming received his bachelor's, master's, and doctor's degrees from Zhejiang University, Peking University, and Massachusetts Institute of Technology. He is currently a research fellow at the Beijing Institute of Mathematical Sciences and Applications, responsible for the artificial intelligence and big data research team. Dr. Zhang has long been engaged in the research, development, and application of artificial intelligence technologies to big data prediction and resource optimization and allocations problems. He presided over the development of digital intelligence service platform "printing and dyeing brain", which was well recognized in the industry. At present, his work focuses on the research and development of digital twins and process twins in the industrial field.