Machine Learning for Partial Differential Equations

I will discuss several ways in which machine learning can be used for solving and understanding the solutions of nonlinear partial differential equations. Part of the talk will focus on learning discretizations for coarse graining the numerical solutions of the Navier Stokes equation. I will also discuss how learned representations can give insight into the nature of the solution manifold for the navier stokes equations, allowing the discovery of new classes of solutions.