Physics-Informed Neural Networks - Wikipedia - Navier–Stokes equations  ## Metadata - Author: **Navier–Stokes equations** - Full Title: Physics-Informed Neural Networks - Wikipedia - Category: #articles - URL: https://en.m.wikipedia.org/wiki/Physics-informed_neural_networks ## Highlights - Physics-informed neural networks (PINNs) are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).[1] They overcome the low data availability of some biological and engineering systems that makes most state-of-the-art machine learning techniques lack robustness, rendering them ineffective in these scenarios.[1] The prior knowledge of general physical laws acts in the training of neural networks (NNs) as a regularization agent that limits the space of admissible solutions, increasing the correctness of the function approximation. This way, embedding this prior information into a neural network results in enhancing the information content of the available data, facilitating the learning algorithm to capture the right solution and to generalize well even with a low amount of training examples