<ul><li>Machine learning methods have shown promise in learning chaotic dynamical systems.</li><li>In large-scale, spatiotemporally chaotic systems, data-driven machine learning methods suffer from inefficiencies.</li><li>Clustered echo state networks, incorporating the spatial coupling structure, outperform existing models in learning chaotic systems.</li><li>The approach remains effective even with imperfect prior coupling knowledge and noise in training data.</li></ul>

Incorporating Coupling Knowledge into Echo State Networks for Learning Spatiotemporally Chaotic Dynamics

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