State Estimation Using Sparse DEIM and Recurrent Neural Networks

• URL: http://arxiv.org/abs/2410.15982v2
• Date: Thu, 10 Jul 2025 21:34:35 GMT
• Title: State Estimation Using Sparse DEIM and Recurrent Neural Networks
• Authors: Mohammad Farazmand,
• Abstract summary: We introduce an equation-free S-DEIM framework that estimates the optimal kernel vector from sparse observational time series.<n>We show that the recurrent architecture is necessary since the kernel cannot be estimated from instantaneous observations.<n>In each case, the resulting S-DEIM estimates are satisfactory even when a relatively simple RNN architecture is used.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Sparse Discrete Empirical Interpolation Method (S-DEIM) was recently proposed for state estimation in dynamical systems when only a sparse subset of the state variables can be observed. The S-DEIM estimate involves a kernel vector whose optimal value is inferred through a data assimilation algorithm. This data assimilation step suffers from two drawbacks: (i) It requires the knowledge of the governing equations of the dynamical system, and (ii) It is not generally guaranteed to converge to the optimal kernel vector. To address these issues, here we introduce an equation-free S-DEIM framework that estimates the optimal kernel vector from sparse observational time series using recurrent neural networks (RNNs). We show that the recurrent architecture is necessary since the kernel vector cannot be estimated from instantaneous observations. But RNNs, which incorporate the past history of the observations in the learning process, lead to nearly optimal estimations. We demonstrate the efficacy of our method on three numerical examples with increasing degree of spatiotemporal complexity: a conceptual model of atmospheric flow known as the Lorenz-96 system, the Kuramoto-Sivashinsky equation, and the Rayleigh-Benard convection. In each case, the resulting S-DEIM estimates are satisfactory even when a relatively simple RNN architecture, namely the reservoir computing network, is used.

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