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Annual Conference of the Japanese Society for Artificial Intelligence

JSAI 2019: Advances in Artificial Intelligence pp 37–44Cite as

Deep Markov Models for Data Assimilation in Chaotic Dynamical Systems

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1128)

Abstract

This is an extension from a selected paper from JSAI2019. Recently, the use of deep learning in data assimilation has been gaining research attention. For instance, the time-series deep Markov model has been proposed along with an inference network trained using variational inference. However, the original proposal did not fully leverage the model ability for data assimilation. Therefore, we aim to evaluate the suitability of a deep Markov model and its inference network for a chaotic dynamical system, which is a common problem in data assimilation. We evaluate the model under various generative conditions. The results show that when information about part of the target model is known, the model has comparable performance to a smoothed unscented Kalman filter, even under the presence of process and observation noise.

Keywords

  • Data assimilation
  • Chaotic system
  • Deep Markov model
  • Variational inference

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Acknowledgements

This work was supported by JSPS KAKENHI under Grant Number JP16K00231.

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Authors and Affiliations

  1. Chiba University, 1-33, Yayoicho, Inage Ward, Chiba-shi, Chiba, 263-8522, Japan

    Calvin Janitra Halim & Kazuhiko Kawamoto

Authors
  1. Calvin Janitra Halim
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  2. Kazuhiko Kawamoto
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Corresponding author

Correspondence to Calvin Janitra Halim .

Editor information

Editors and Affiliations

  1. Department of Systems Innovation, University of Tokyo, Tokyo, Japan

    Prof. Yukio Ohsawa

  2. Faculty of Business and Commerce, Kansai University, Osaka, Japan

    Prof. Katsutoshi Yada

  3. Nagoya Institute of Technology, Nagoya, Japan

    Prof. Takayuki Ito

  4. Graduate School of System Design, Tokyo Metropolitan University, Tokyo, Japan

    Prof. Yasufumi Takama

  5. Department of Information and Communication, Tokyo Metropolitan University, Tokyo, Japan

    Dr. Eri Sato-Shimokawara

  6. Faculty of Letters, Chiba University, Chiba, Japan

    Prof. Akinori Abe

  7. School of Engineering, The University of Tokyo, Tokyo, Japan

    Prof. Junichiro Mori

  8. Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan

    Prof. Naohiro Matsumura

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Halim, C.J., Kawamoto, K. (2020). Deep Markov Models for Data Assimilation in Chaotic Dynamical Systems. In: , et al. Advances in Artificial Intelligence. JSAI 2019. Advances in Intelligent Systems and Computing, vol 1128. Springer, Cham. https://doi.org/10.1007/978-3-030-39878-1_4

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