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.

[PNAS 2025] The official implementation of the paper "Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design".

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

🛰️ Production-ready ML system for geomagnetic storm prediction | 98% AUC, 70% recall | Threshold-optimized ensemble with real-time inference | 29-year dataset (1996-2025) | NOAA SWPC operational standards | Complete MLOps pipeline

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

Deep learning library for solving differential equations and more

Hidden physics models: Machine learning of nonlinear partial differential equations