dl-wg Goto Github PK

acoustic_emission_-_ml

adversarial-ae-lstm

Using adversarial training to improve forecasts of data-driven surrogate models

airqualitydisease

breathe-in-breathe-out

cmcc

da-tutorial-

introduction to DA

da_material

data_assimilation

MSc Research project (6 months). Data Assimilation using Deep Learning (AEs). Imperial College Machine Learning MSc 2018-19

datalearning-seminars

datalearning-seminars.github.io

datalearningparadigm

Code repository for the data learning paper

diameter_inverse

digital-twin-la-global-wildfire

digital_twin_la_global_wildfire

dl-wg.github.io

dl_surrogate_jules_inferno

drop-coalescence-surrogate-model-and-la

dscvae-for-drop-coalescence

explainable-ai-for-drop-coalescence

fidn

Fire-Image-DenseNet - A model for predicting the final burnt area of wildfire

flow-da

gcn-lstm-wildfire

gla_with_rosm

In this project, we introduce the Generalised Latent Assimilation (GLA) approach which can be used in Machine learning surrogate modelling for dynamical systems

hpc-training

Python and bash script for training on HPC

latent-gan

Code base for Latent-GAN training algorithm.

latentassimilation

latentgan

magic-roda

This repository includes the Reduced Order Model code and the Variational Data Assimilation code developed for the EPSRC MAGIC (EP/N010221/1) project

medla-multi-domain-encoder-decoder-neural-networks-for-latent-data-assimilation-in-dynamical-systems

ml_surrogate_model_wildfires

ML surrogate model which combines ROM, LSTM and DA for dynamical systems such as wildfires