Generative network-based reduced-order model for prediction, data assimilation and uncertainty quantification

Vinicius Luiz Santos Silva, Claire E Heaney, Christopher Charles Pain

Keywords: uncertainty quantification, data assimilation, inverse problem, generative networks, generative adversarial network, deep learning, reduced-order model

TL;DR: New approach that integrates a generative network into a reduced-order model to solve inverse problems for numerical physical simulations.

Abstract: We propose a new method in which a generative network (GN) integrate into a reduced-order model (ROM) framework is used to solve inverse problems for partial differential equations (PDE). The aim is to match available measurements and estimate the corresponding uncertainties associated with the states and parameters of a numerical physical simulation. The GN is trained using only unconditional simulations of the discretized PDE model. We compare the proposed method with the golden standard Markov chain Monte Carlo. We apply the proposed approaches to a spatio-temporal compartmental model in epidemiology. The results show that the proposed GN-based ROM can efficiently quantify uncertainty and accurately match the measurements and the golden standard, using only a few unconditional simulations of the full-order numerical PDE model.

Submission Type: Non-Archival

Submission Number: 14