ABSTRACT.
In this talk, we present a comprehensive study of both classical and modern numerical methods for solving partial differential equations (PDEs). We begin by employing finite element methods to solve fluid equations and subsequently delve into the theoretical analysis of the numerical method. In particular, we focus on a system of nonlinear PDEs that models the motion of an incompressible generalized Newtonian fluid with non-standard growth conditions. We construct a finite element approximation of the model and rigorously analyze the associated numerical method. Key technical tools include discrete counterparts of the Bogovskii operator, De Giorgi’s regularity theorem in two dimensions, and the Acerbi-Fusco Lipschitz truncation technique for Sobolev functions in function spaces with variable integrability exponents. As a natural extension of classical methods, we further explore modern approaches for solving PDEs using deep learning. Recent advancements in machine learning, particularly deep neural networks, have demonstrated remarkable success across various disciplines. In the second part of this talk, I will present my recent research on solving non-Newtonian fluid equations using deep learning. This method first computes the velocity using the Deep Ritz Method in a divergence-free formulation, and then recovers the pressure using Physics-Informed Neural Networks based on de Rham theory. I will also introduce a theoretical analysis of this new method.