Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology
© The Author(s) 2022.
| Publié dans: | Journal of scientific computing. - 1999. - 94(2023), 1 vom: 31., Seite 25 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2023
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| Accès à la collection: | Journal of scientific computing |
| Sujets: | Journal Article COVID-19 Compartmental model Data assimilation Deep learning Epidemiology Generative adversarial networks Reduced-order model Spatio-temporal prediction |
| Résumé: | © The Author(s) 2022. We propose a novel use of generative adversarial networks (GANs) (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. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, 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 |
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| Description: | Date Revised 15.09.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 0885-7474 |
| DOI: | 10.1007/s10915-022-02078-1 |