Abstract: In this talk, I will review how we can use neural networks to solve differential equations for initial conditions and boundary value problems. There are many exciting directions in this line of work, but two main criticisms are computational complexity compared to traditional methods and the lack of error bounds. I will present transfer learning methods that can be used to speed up computation and new work on estimating the error bounds of neural network approach in solving (partial) differential equations.