edsml-bpg23

simulai

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

GFN

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

gca-rom

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

Graph_AE

graph_unpooling

pytorch_geometric

Graph Neural Network Library for PyTorch

FBPINNs

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

latentpinn

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

WienerLoss

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

DL_Surrogate_JULES_INFERNO

stride

A modelling and optimisation framework for medical ultrasound

pinn_fwi

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

discovering_modern_cpp

Source codes of Discovering Modern C++

AE-ConvLSTM-Flow-Dynamics

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 to predict 5 to 10-time steps in one pass through the neural network. The network is trained for unsteady fluid simulations using data. Another training method tested is the physics constraint method, where governing equations of fluid motion are used to optimize loss. Few attempts to train unsteady Navier-Stokes are made, but it dint work.

GAN_Video_Prediction

LIFD_Physics_Informed_Neural_Networks

PDEBench

PDEBench: An Extensive Benchmark for Scientific Machine Learning

jtk

The Mines Java Toolkit

harmonic-oscillator-pinn-workshop

Introductory workshop on PINNs using the harmonic oscillator

hands-on-pinns

A Hands-on Introduction to Physics-Informed Neural Networks

neuraloperator

Learning in infinite dimension with neural operators.

harmonic-oscillator-pinn

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

advent-of-code

xarray

N-D labeled arrays and datasets in Python

experiments

Codebase for reproducible benchmarking experiments in MedMNIST v2

introduction-to-python

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