Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Code accompanying my blog post: So, what is a physics-informed neural network?

Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

A Physics-Informed Neural Network to solve 2D steady-state heat equations.

Applications of PINOs

No need to train, he's a smooth operator

Learning function operators with neural networks.

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

This repo contains the code for solving Poisson Equation using Physics Informed Neural Networks

A Physics-informed neural network (PINN) library.

Supporting code for "reduced order modeling using advection-aware autoencoders"

Going through the tutorial on Physics-informed Neural Networks: https://github.com/madagra/basic-pinn

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

Physics Informed Neural Networks - research in problem solving, architecture improvements, new applications

Hidden physics models: Machine learning of nonlinear partial differential equations

Π-ML: Learn data-driven similarity theories of physical problems

Physics-based machine learning with dynamic Boltzmann distributions

Code for paper "Physics-based machine learning for modeling IP3 induced calcium oscillations" - DOI: 10.5281/zenodo.4839127

Deep learning library for solving differential equations and more

Includes codes for, "Learning to generate synthetic human mobility data: A physics-regularized Gaussian process approach based on multiple kernel learning"