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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
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?
Codebase for reproducible benchmarking experiments in MedMNIST v2
"Introduction to Python" course for future Imperial College London MSc students