Evolutionary optimization reveals structural constraints on reservoir architectures for spatiotemporal chaos
neurovium · x · 2026-10-15
Nima Dehghani's arXiv paper evolves reservoir computing architectures for predicting Kuramoto–Sivashinsky spatiotemporal chaos across five construction hyperparameters (size, connectivity, spectral radius, input scaling, readout regularization). Evolution cut population-level prediction error and extended low-error forecast horizons; evolved reservoirs stayed within a conserved stochastic-block-model-like spectral envelope, locked modularity to an intermediate band, and pruned connection costs within it. Pareto analysis shows elite reservoirs achieve accuracy and efficiency jointly rather than via a simple trade-off.
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