Kernel learning backward SDE filter for data assimilation

Archibald, Richard; Bao, Feng

doi:10.1016/j.jcp.2022.111009

Title: Kernel learning backward SDE filter for data assimilation

Journal Article · 28 January 2022 · Journal of Computational Physics

DOI: https://doi.org/10.1016/j.jcp.2022.111009 · OSTI ID:1865130

Archibald, Richard ^[1]; Bao, Feng ^[2]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); (850) 644-2202
Florida State Univ., Tallahassee, FL (United States)

In this paper, we develop a kernel learning backward SDE filter method to estimate the state of a stochastic dynamical system based on its partial noisy observations. A system of forward backward stochastic differential equations is used to propagate the state of the target dynamical model, and Bayesian inference is applied to incorporate the observational information. Further, to characterize the dynamical model in the entire state space, we introduce a kernel learning method to learn a continuous global approximation for the conditional probability density function of the target state by using discrete approximated density values as training data. Numerical experiments demonstrate that the kernel learning backward SDE is highly effective.

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Research Organization:: Florida State Univ., Tallahassee, FL (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: AC05-00OR22725; SC0022297

OSTI ID:: 1865130

Journal Information:: Journal of Computational Physics, Journal Name: Journal of Computational Physics Vol. 455; ISSN 0021-9991

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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