Reservoir computing enables one-shot prediction of entire noise-induced bifurcation diagrams from single-noise time series data.
and Girvan, Michelle and Ott, Edward , title =
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Evolutionary selection on reservoir size, connectivity, spectral radius, input scaling, and regularization for Kuramoto-Sivashinsky forecasting reveals a conserved stochastic-block-model spectral envelope, locked intermediate modularity, and a horizontal cost-modularity floor in elite architectures.
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One-shot prediction of noise-induced bifurcations with reservoir computing
Reservoir computing enables one-shot prediction of entire noise-induced bifurcation diagrams from single-noise time series data.
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Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos
Evolutionary selection on reservoir size, connectivity, spectral radius, input scaling, and regularization for Kuramoto-Sivashinsky forecasting reveals a conserved stochastic-block-model spectral envelope, locked intermediate modularity, and a horizontal cost-modularity floor in elite architectures.