Physics-Informed Neural Networks and Extensions
Raissi, Maziar, Paris Perdikaris, Nazanin Ahmadi Daryakenari, George Em Karniadakis · arXiv (Cornell University) · 2024
In this paper, we review the new method Physics-Informed Neural Networks (PINNs) that has become the main pillar in scientific machine learning, we present recent practical extensions, and provide a specific example in data-driven discovery of governing differential equations.