<ul><li>Researchers have developed a new method called SINDy-SHRED for modeling real-world spatio-temporal data.</li><li>SINDy-SHRED utilizes Gated Recurrent Units to model sparse sensor measurements and a shallow decoder network to reconstruct the full spatio-temporal field.</li><li>The algorithm introduces a SINDy-based regularization for converging to a linear Koopman-SHRED model.</li><li>SINDy-SHRED outperforms current baseline deep learning models in accuracy, training time, and data requirements for video predictions.</li></ul>

Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks

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