What is PINNs?

Physics-Informed Neural Networks (PINNs) are a type of neural network that incorporates physical laws, described by partial differential equations (PDEs), into the network training process. They are commonly used to solve and analyze PDEs, enabling tasks like forward problem solving (predicting the solution given the equation and boundary conditions), inverse problem solving (identifying parameters of the equation from observed data), and data-driven discovery of governing equations. By embedding physics into the learning process, PINNs often achieve higher accuracy and require less training data compared to traditional neural networks for similar tasks in scientific computing.