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

• URL: http://arxiv.org/abs/2105.07729v1
• Date: Mon, 17 May 2021 10:56:53 GMT
• Title: Data Assimilation Predictive GAN (DA-PredGAN): applied to determine the spread of COVID-19
• Authors: Vinicius L S Silva, Claire E Heaney, Yaqi Li, Christopher C Pain
• Abstract summary: We propose the novel use of a generative adversarial network (GAN) to make predictions in time (PredGAN) and to assimilate measurements (DA-PredGAN) GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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