Physics Informed Neural Networks (PINNs): An Intuitive Guide (medium.com)

A new guide on Physics Informed Neural Networks (PINNs) aims to clarify their functions and benefits for the AI/ML community, particularly for those faced with complex scientific equations. Traditional physics modeling requires expert parametrization, while purely data-driven approaches struggle with incomplete or noisy data. PINNs uniquely combine these methods, utilizing both data-driven learning and established physics equations, resulting in models that are more robust and capable of extrapolating beyond limited datasets. The guide illustrates the application of PINNs using projectile motion, a concept described through both physics equations and neural network training. By incorporating physics as a regularization term during the training process, PINNs can minimize errors in model predictions while adhering to known physical laws—such as the relationship between displacement, velocity, and acceleration. This innovative approach not only aids in parameter discovery, like determining drag coefficients from data, but also enables users to generate accurate approximate solutions even when closed-form equations are unattainable. Overall, this intuitive understanding of PINNs has significant implications for researchers and practitioners aiming to enhance model accuracy in scientific computing and simulation tasks.