Machine Learning for Partial Differential Equations

Abstract: I will discuss several ways in which machine learning can be used for solving and understanding the solutions of nonlinear partial differential equations. Most of the talk will focus on learning discretizations for coarse graining the numerical solutions of PDEs. I will start with examples in 1d, and then move on to advection/diffusion in a turbulent flow and then the Navier Stokes equation.

machine learningdynamical systemsapplied physics

Audience: researchers in the topic

Workshop on Scientific-Driven Deep Learning (SciDL)

Series comments: When: 8:00-14:30pm (PST) on Wednesday July 1, 2020 Where: berkeley.zoom.us/j/95609096856 Details: scidl.netlify.app/