Competitive Physics Informed Networks

Physics-Informed Neural networks for Advanced modeling

A toolkit with data-driven pipelines for physics-informed machine learning.

Graph Feedforward Networks: a resolution-invariant generalisation of feedforward networks for graphical data, applied to model order reduction

GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.

Graph Neural Network Library for PyTorch

Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)

Official repository for the "Multiple wavefield solutions in physics-informed neural networks using latent representation" paper.

Implementation of an adaptive weiner filter as a loss function for autoencoders and variational autoencoders

A modelling and optimisation framework for medical ultrasound

PGNN-FWI: performing physics-guided neural network for FWI

Source codes of Discovering Modern C++

This repository contains an Auto-encoder ConvLSTM network (Pytorch) which can be used to predict a large number of time steps (100+). The network prediction is sequence-to-sequence which works well…

PDEBench: An Extensive Benchmark for Scientific Machine Learning

The Mines Java Toolkit

Introductory workshop on PINNs using the harmonic oscillator

A Hands-on Introduction to Physics-Informed Neural Networks

Learning in infinite dimension with neural operators.

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

N-D labeled arrays and datasets in Python

Codebase for reproducible benchmarking experiments in MedMNIST v2

"Introduction to Python" course for future Imperial College London MSc students