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

Introductory workshop on PINNs using the harmonic oscillator

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).

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

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.

JAX library for neural operator and PDE foundation model training.

"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.

📕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.

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

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

Splinter is a lock-free shared memory bus that puts your AI governor in the same room (or even NUMA lane) as your model and inference. It even *includes* socket-free inference sidecars with more powerful tools that teach you how to build with it. Splinter fits in the size of most modern CPU instruction caches (876 ELOC W/Excellent Test Coverage) .

My work on Physics Informed Neural Networks (PINNs). Continuous and Discrete time models along with exploring implementing models for Coupled PDEs.

Generative AI framework for Industrial Tomography, using Physics-Informed Shape Morphing to reconstruct internal flow dynamics from sparse sensor data

Code associated with my Foundations in AI Essay on Physics Informed Machine Learning (PIML), TB2 Spring 2026.

📝A physics-informed Machine Learning model designed to predict candle extinction time across different wax types (Paraffin, Soy, Beeswax) using volumetric burn rate analysis and regression modeling.

Design Science Research prototype for mobile detection and localization of New Psychoactive Substances (NPS), integrating physics-informed machine learning, atmospheric dispersion modeling, MLOps, and forensic auditability.