Abstract: It has been shown that neural networks can be used to approximate any function from $\mathbb{R}^n$ to $\mathbb{R}^m$ with compact support arbitrary well. This motivates the usage of neural networks to approximate the solution to a PDE. Such ideas were introduced in the late 1990's but only today's modern computational ability allows for this approach to be a viable option. In this presentation, I will show how traditional numerical methods can be limited by stepsize and reliance on mesh creation; then I'll introduce physics-informed neural networks (PINNs): a deep-learning framework for solving PDEs. The idea is to utilize the physical properties of a PDE to train a neural network that can approximate the solution to the equation. To this end, I will show the implementation of this scheme for both forward and inverse problems, compute the solutions to test examples, and point out the limitations and recent developments regarding this framework.