- Maziar Raissi Github - Solving different pde's using PINN
- Computational graphs and Backpropagation - Blog post on what are computational graphs by Christoph Olah
- Numerical Optimization: Understanding L-BFGS- Blog post on the mathematics behind the L-BFGS optimization method by Aria Haghighi
- Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations - Introducing physics informed neural networks and their forward usage for surrogate modeling
- Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations - Introducing physics informed neural netwroks with focus on data driven discovery of differential equations
- SciANN: A Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks - A python package using keras and tensorflow for solving pdes' and differential equation data driven discovery
- Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations - A scalable approach to for solving large problems related to differential equations with pinns and finding a set of basis functions
- Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture - Automated hyperparameter optimization approach for PINNs'
- One-Shot Transfer Learning of Physics-Informed Neural Networks - A general framework for transfer-learning on PINNs
- Who Invented the Reverse Mode of Differentiation? - On history and introduction to autodifferentiation
- Introduction to Physics Informed Neural Networks - A hands on solution to logistic equation using PINN
- So, what is a physics-informed neural network? - A tutorial on harmonic oscillator problem solution using PINNs