Dokumentdetails
ID
oai:arXiv.org:2405.18674
Thema
Computer Science - Machine Learnin... Physics - Atmospheric and Oceanic ... Physics - Data Analysis, Statistic...Autor
Tarumi, Yuta Fukuda, Keisuke Maeda, Shin-ichiKategorie
Computer Science
Jahr
2024
Auflistungsdatum
09.10.2024
Schlüsselwörter
physical data physics posteriors assimilation spaceZusammenfassung
State estimation for nonlinear state space models (SSMs) is a challenging task.
Existing assimilation methodologies predominantly assume Gaussian posteriors on physical space, where true posteriors become inevitably non-Gaussian.
We propose Deep Bayesian Filtering (DBF) for data assimilation on nonlinear SSMs.
DBF constructs new latent variables $h_t$ in addition to the original physical variables $z_t$ and assimilates observations $o_t$.
By (i) constraining the state transition on the new latent space to be linear and (ii) learning a Gaussian inverse observation operator $r(h_t|o_t)$, posteriors remain Gaussian.
Notably, the structured design of test distributions enables an analytical formula for the recursive computation, eliminating the accumulation of Monte Carlo sampling errors across time steps.
DBF trains the Gaussian inverse observation operators $r(h_t|o_t)$ and other latent SSM parameters (e.g., dynamics matrix) by maximizing the evidence lower bound.
Experiments demonstrate that DBF outperforms model-based approaches and latent assimilation methods in tasks where the true posterior distribution on physical space is significantly non-Gaussian.
;Comment: Main text 10 pages
Tarumi, Yuta,Fukuda, Keisuke,Maeda, Shin-ichi, 2024, Deep Bayesian Filter for Bayes-faithful Data Assimilation
Deep Bayesian Filter for Bayes-faithful Data Assimilation
physical data physics posteriors assimilation space
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