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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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Correspondence to Calvin Janitra Halim .

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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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