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Poster
in
Workshop: Machine Learning and the Physical Sciences

Model Inversion for Spatio-temporal Processes using the Fourier Neural Operator

Daniel MacKinlay · Daniel Pagendam · Petra Kuhnert


Abstract:

We explore model inversion using the Fourier Neural Operator (FNO) of Li et al. The approach learns a FNO emulator of the partial differential equation forward operator from simulated realisations and then the latent inputs (physical system parameters) are selected by solving an optimisation to match a set of observations. Our results suggest that this underdetermined inverse problem is substantially harder but by careful regularisation we are able to improve our inference substantially.

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