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

arXiv: https://arxiv.org/abs/2105.07729 · HTML: https://mlsanity.com/paper/2105.07729/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 80 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 18 of 18)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Automatic differentiation in machine learning: a survey](https://mlsanity.com/paper/1502.05767/) (2015)
- [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https://mlsanity.com/paper/1803.10892/) (2018)
- [A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs](https://mlsanity.com/paper/2001.04001/) (2020)
- [Multi-level Convolutional Autoencoder Networks for Parametric Prediction of Spatio-temporal Dynamics](https://mlsanity.com/paper/1912.11114/) (2019)
- [Data driven approximation of parametrized PDEs by Reduced Basis and Neural Networks](https://mlsanity.com/paper/1904.01514/) (2019)
- [Deep Generative Model-based Quality Control for Cardiac MRI Segmentation](https://mlsanity.com/paper/2006.13379/) (2020)
- [An autoencoder-based reduced-order model for eigenvalue problems with application to neutron diffusion](https://mlsanity.com/paper/2008.10532/) (2020)
- [Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network](https://mlsanity.com/paper/2004.00707/) (2020)
- [Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training](https://mlsanity.com/paper/1906.01984/) (2019)
- [Recent Developments Combining Ensemble Smoother and Deep Generative Networks for Facies History Matching](https://mlsanity.com/paper/2005.10638/) (2020)
- [Non-intrusive Surrogate Modeling for Parametrized Time-dependent PDEs using Convolutional Autoencoders](https://mlsanity.com/paper/2101.05555/) (2021)
- [Digital twins based on bidirectional LSTM and GAN for modelling the COVID-19 pandemic](https://mlsanity.com/paper/2102.02664/) (2021)
- [DeepFlow: History Matching in the Space of Deep Generative Models](https://mlsanity.com/paper/1905.05749/) (2019)
- [Bifidelity data-assisted neural networks in nonintrusive reduced-order modeling](https://mlsanity.com/paper/1902.00148/) (2019)
- [Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in European countries: technical description update](https://mlsanity.com/paper/2004.11342/) (2020)
- [An Epidemiological Modelling Approach for Covid19 via Data Assimilation](https://mlsanity.com/paper/2004.12130/) (2020)

## Built on by (most cited first, 4 of 4)

- [Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models](https://mlsanity.com/paper/2204.03497/) (2022)
- [An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes](https://mlsanity.com/paper/2202.06170/) (2022)
- [Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty Quantification](https://mlsanity.com/paper/2105.13859/) (2021)
- [Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer](https://mlsanity.com/paper/2310.03770/) (2023)

## Abstract (arXiv)

We propose the novel use of a generative adversarial network (GAN) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. To predict the spread of COVID-19 in an idealised town, we apply these methods to a compartmental model in epidemiology that is able to model space and time variations. To do this, the GAN is set within a reduced-order model (ROM), which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.