In this talk, we first introduce the concept of implicit bias in deep learning. We then discuss the implicit biases present in commonly used neural network-based PDE solvers, offering new insights into the optimization and generalization of these algorithms. Finally, we present some data-driven approaches for solving multi-scale PDE problems and discuss the associated issues of implicit bias, optimization, and generalization.
