Deep Markov Models for Data Assimilation in Chaotic Dynamical Systems

• HALIM Calvin Janitra

  Chiba University

• KAWAMOTO Kazuhiko

  Chiba University

抄録

Recently, the use of deep learning in data assimilation has been gaining traction. One particular time series model known as deep Markov model has been proposed, along with an inference network that is trained together using variational inference. However, the original paper did not address the full capability of the model in data assimilation problem. Therefore, we aim to evaluate the suitability of a deep Markov model and its inference network against a chaotic dynamical system, which often shows up as a problem in data assimilation. We evaluate the model in various generative conditions. We show that when information about part of the target model is known, the model is able to match the capability of a smoothed unscented Kalman filter, even when there are process and observation noise involved.

収録刊行物

• 人工知能学会全国大会論文集

  人工知能学会全国大会論文集 JSAI2019 (0), 2H5E203-2H5E203, 2019

  一般社団法人 人工知能学会

キーワード

• Data Assimilation
• Deep Markov Model

詳細情報

• CRID: 1390282752372130944
• NII論文ID: 130007658483
• DOI: 10.11517/pjsai.jsai2019.0_2h5e203
• 本文言語コード: en
• データソース種別:
  • JaLC
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可