A Hands-on Introduction to Physics-informed Machine Learning

Ilias Bilionis, Atharva Hans, Purdue University Table of Contents below. This video is part of NCN’s Hands-on Data Science and Machine Learning Training Series which can be found at: Can you make a neural network satisfy a physical law? There are two main types of these laws: symmetries and ordinary/partial differential equations. I will focus on differential equations in this short presentation. The simplest way to bake information about a differential equation with neural networks is to create a regularization term for the loss function used in training. I will explain the mathematics of this idea. I will also talk about applying physics-informed neural networks to a plethora of applications spanning the range from solving differential equations for all possible parameters in one sweep (e.g., solve for all boundary conditions) to calibrating differential equations using data to design optimization. Then, we will work on a hands-on activity that sho