Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations

• URL: http://arxiv.org/abs/2108.03561v1
• Date: Sun, 8 Aug 2021 03:38:36 GMT
• Title: Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations
• Authors: Georg A. Gottwald and Sebastian Reich
• Abstract summary: We present a supervised learning method to learn the propagator map of a dynamical system from partial and noisy observations. We show that the combination of random feature maps and data assimilation, called RAFDA, outperforms standard random feature maps for which the dynamics is learned using batch data.
• Score: 0.76146285961466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a supervised learning method to learn the propagator map of a dynamical system from partial and noisy observations. In our computationally cheap and easy-to-implement framework a neural network consisting of random feature maps is trained sequentially by incoming observations within a data assimilation procedure. By employing Takens' embedding theorem, the network is trained on delay coordinates. We show that the combination of random feature maps and data assimilation, called RAFDA, outperforms standard random feature maps for which the dynamics is learned using batch data.

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