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

Introductory workshop on PINNs using the harmonic oscillator

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

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

[ICML 2025] A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems

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

Deep learning library for solving differential equations and more

Hidden physics models: Machine learning of nonlinear partial differential equations

This repository contains all Assignments and Lecture Slides from the Physics Informed Machine learning course by Prof. Augustin Guibaud in Spring 2025 at NYU.