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Arxiv
1d
166
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State Estimation Using Sparse DEIM and Recurrent Neural Networks
- Sparse Discrete Empirical Interpolation Method (S-DEIM) is used for state estimation in dynamical systems with sparse observations.
- An equation-free S-DEIM framework is introduced that utilizes recurrent neural networks (RNNs) to estimate the optimal kernel vector from sparse observational time-series data.
- RNNs incorporate past observations to improve estimations as the optimal kernel vector cannot be estimated from instantaneous data.
- The method's efficacy is demonstrated on atmospheric flow, Kuramoto-Sivashinsky equation, and Rayleigh-Benard convection, showing satisfactory results with a simple RNN architecture.
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