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.
📕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
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)
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).
Short review of Physics-Informed ML/DL
Research Group Website
This repository contains all Assignments and Lecture Slides from the Physics Informed Machine learning course by Prof. Augustin Guibaud in Spring 2025 at NYU.
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