Physics Informed Neural Networks (PINNs): An Intuitive Guide
<p>If you’ve ever tried to read existing literature on physics informed neural networks (PINNs), it’s a tough read! Either lots of equations that for most people will be unfamiliar and assumptions that you are already an expert with all of the concepts, or too simplistic to gain a good understanding. This post aims to walk through PINNs in an intuitive way, and also suggests some improvements over current literature.</p>
<p><strong>Traditional physics model</strong> creation is a task of a domain expert, who parametrises physics models to best fit a system of interest. For example, creating a model of aircraft dynamics using equations of drag, lift, gravity, thrust, etc., and parametrising the model to attempt to closely match the model to a specific aircraft.</p>
<p><a href="https://towardsdatascience.com/physics-informed-neural-networks-pinns-an-intuitive-guide-fff138069563"><strong>Read More</strong></a></p>