Code accompanying my blog post: So, what is a physics-informed neural network?
AeroJAX: A differentiable, structure-preserving framework for real-time flow simulation, control, and inverse design. Architected for neural operator integration and latent-space acceleration. Built with JAX.
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.
Curso teórico práctico sobre inversión geofísica en Python: SimPEG, gravimetría, magnetometría, MT 1D y FWI.
"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.
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
Falsification-first scientific formula discovery with LLMs, symbolic regression, dimensional analysis, Pareto selection, and LLM-SRBench evaluation. Codex API by default.
NeedForHat Diagnosis: physics informed machine learning models for the residential energy transition
Thermophysical data workbench for generating, importing, comparing, validating and visualizing data from experiments, literature, property models and simulation backends, with leakage-aware preparation for physics-informed machine learning.
This repository contains all Assignments and Lecture Slides from the Physics Informed Machine learning course by Prof. Augustin Guibaud in Spring 2025 at NYU.
This is a repo for my MSc Machine Learning Final Project. Research on PINN, DRM and WAN
LieNLSD: explicit nonlinear Lie symmetry discovery from dynamical-system and PDE data; discovers Lie algebra dimension and symbolic infinitesimal generators. ICML 2025.
A comparative analysis of DeepONet and FNO architectures, benchmarking their performance on Function-to-Function (Heat Equation) vs. Parameter-to-Function (Elastic Bar) PDE problems to motivate hybrid operator designs.
[ICASSP 2026] Official PyTorch Implementation of "Modularity-Free Conflict-Averse Training for Generalized PINNs"
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