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

Introductory workshop on PINNs using the harmonic oscillator

Official implementation of Neural Lithography (SIGGRAPH Asia 2023)

Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.

A physics-informed deep learning (DL)-based constitutive model for investigating epoxy based composites under different ambient conditions.

A carefully curated collection of high-quality libraries, projects, tutorials, research papers, and other essential resources focused on Physics-Informed Machine Learning (PIML) and Physics-Informed Neural Networks (PINNs).

📕Code for paper Parallelizable Complex Neural Dynamics Models for PMSM Temperature Estimation with Hardware Acceleration.

NeedForHat Diagnosis: physics informed machine learning models for the residential energy transition

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

"Bayesian Enhanced AoA Estimator: A Physics-Informed Machine Learning Approach for Accurate Angle of Arrival Estimation". This repository is an AoA estimator for passive UHF RFID based on Bayesian regression and classical antenna array signal processing. Combines physics-informed analysis with Pyro-based uncertainty quantification.

Workshop on designing efficient and scalable physics-informed neural networks with JAX

Main codes for half-cell model, PINN and co-kriging implemented for physics-informed degradation diagnostics project: https://doi.org/10.1016/j.ensm.2024.103343

iHealth workshop about applications of physics-informed neural networks (PINNs)

This is a repo for my MSc Machine Learning Final Project. Research on PINN, DRM and WAN

Short review of Physics-Informed ML/DL

Research Group Website