PhysicsInformed
Artificial Intelligence
Essential Review Papers on Physics-Informed Neural Networks: A Curated Guide for Practitioners
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...
Artificial Intelligence
Discovering Differential Equations with Physics-Informed Neural Networks and Symbolic Regression
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...
Artificial Intelligence
Unraveling the Design Pattern of Physics-Informed Neural Networks: Part 05
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...
Artificial Intelligence
Unraveling the Design Pattern of Physics-Informed Neural Networks: Series 01 1. Paper at a look: 2. Design pattern 3 Potential Future Improvements 4 Takeaways Reference
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...
Artificial Intelligence
Unraveling the Design Pattern of Physics-Informed Neural Networks: Series 01
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...
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