2019年度 人工知能学会全国大会（第33回）
2019年6月4日〜6月7日朱鷺メッセ 新潟コンベンションセンター
2019年度 人工知能学会全国大会（第33回）
2019年6月4日〜6月7日朱鷺メッセ 新潟コンベンションセンター
[2H5-E-2-03]Deep Markov Models for Data Assimilation in Chaotic Dynamical Systems
〇Calvin Janitra Halim1, Kazuhiko Kawamoto1(1. Chiba University)
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.
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.