Deep Markov Models for Data Assimilation in Chaotic Dynamical Systems
Deep Markov Models for Data Assimilation in Chaotic Dynamical Systems
复制标题
混沌动力系统中数据同化的深度马尔可夫模型
DOI：
10.1007/978-3-030-39878-1_4
复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Kawamoto Kazuhiko
中科院分区：
文献类型：
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作者：
Halim Calvin Janitra;Kawamoto Kazuhiko
这是从JSAI2019中选择的论文的扩展。最近，深度学习在数据同化中的应用受到了研究的关注。例如，时间序列深度马尔可夫模型已经被提出沿着使用变分推理训练的推理网络。然而，最初的建议并没有充分利用模式的数据同化能力。因此，我们的目标是评估一个深马尔可夫模型及其推理网络的混沌动力系统，这是一个常见的问题，在数据同化的适用性。我们在各种生成条件下评估模型。结果表明，当目标模型的部分信息是已知的，该模型具有可比的性能，平滑无迹卡尔曼滤波器，即使在过程和观测噪声的存在下。
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.