PHYSICS INFORMED MODEL ERROR FOR DATA ASSIMILATION

JULES GUILLOT; EMMANUEL FRENOD; PIERRE AILLIOT

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PHYSICS INFORMED MODEL ERROR FOR DATA ASSIMILATION

机译：PHYSICS INFORMED MODEL ERROR FOR DATA ASSIMILATION

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摘要

Data assimilation consists in combining a dynamical model withnoisy observations to estimate the latent true state of a system. The dynamicalmodel is generally misspecified and this generates a model error which is usuallytreated using a random noise. The aim of this paper is to suggest a newtreatment for the model error that further takes into account the physics ofthe system: the physics informed model error. This model error treatment isa noisy stationary solution of the true dynamical model. It is embedded in theensemble Kalman filter (EnKF), which is a usual method for data assimilation.The proposed strategy is then applied to study the heat diffusion in a barwhen the external heat source is unknown. It is compared to usual methods toquantify the model error. The numerical results show that our method is moreaccurate, in particular when the observations are available at a low temporalresolution.

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《Discrete and continuous dynamical systems, Series S》 |2023年第2期|265-276|共12页
作者
JULES GUILLOT; EMMANUEL FRENOD; PIERRE AILLIOT;
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作者单位

Univ Bretagne - Sud, CNRS UMR 6205, LMBA, F-56000 Vannes, France;

Univ Brest, CNRS UMR 6205Laboratoire de Mathematiques de Bretagne Atlantique, France;

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正文语种英语
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Data assimilation; EnKF; heat equation; model error; uncertainty quantification;

PHYSICS INFORMED MODEL ERROR FOR DATA ASSIMILATION

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