PhysicsInformed

Staying on top of a fast-growing research field is rarely easy. I face this challenge firsthand as a practitioner in Physics-Informed Neural Networks (PINNs). Latest papers, be they algorithmic advancements or cutting-edge applications, are published...

A case study with step-by-step code implementation25 min read·11 hours agoSuch partial knowledge of the governing differential equations hinders our understanding and control of those dynamical systems. Consequently, inferring these unknown components based on...

2.1 Problem 🎯In the applying of Physics-Informed Neural Networks (PINNs), it comes as no surprise that the neural network hyperparameters, comparable to network depth, width, the selection of activation function, etc, all have significant...

2.1 ProblemPhysics-Informed Neural Networks (PINNs) offer a definite advantage over conventional neural networks by explicitly integrating known governing atypical or partial differential equations (ODEs/PDEs) of physical processes. The enforcement of those governing equations in...

2.1 ProblemPhysics-Informed Neural Networks (PINNs) offer a definite advantage over conventional neural networks by explicitly integrating known governing abnormal or partial differential equations (ODEs/PDEs) of physical processes. The enforcement of those governing equations in...