wcswcswcs / gan

Generative adversarial networks (GAN) for time series prediction, data assimilation and uncertainty quantification

GAN for time series prediction, data assimilation and uncertainty quantification

This repository is the official implementation of:

Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic (for the Predictive GAN).

Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19.

GAN for time series prediction, data assimilation and uncertainty quantification.

Directories:

• PredGAN: Prediction using GAN - applied to the spatio-temporal spread of COVID-19 in an idealized town.
• DA-PredGAN: Data assimilation using GAN - applied to the spatio-temporal spread of COVID-19 in an idealized town.
• UQ-PredGAN: Uncertainty quantification using GAN - applied to the spatio-temporal spread of COVID-19 in an idealized town.
• datasets: Datasets of the spatio-temporal spread of COVID-19 in an idealized town.

Requirements

To install requirements:

 $ conda env create -f environment.yml 
 $ conda activate py3ml
 $ python -m ipykernel install --user --name=python3

Finally, start Jupyter:

 $ jupyter notebook