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Achieving 卤0.191% accuracy in predicting battery state-of-health could redefine standards for battery performance modeling.
Optimizing hyper-parameters in ridge regression can significantly enhance the reliability of parameter estimation in complex nonlinear models.
fSRD achieves highly accurate linear reconstructions of chaotic systems by adaptively learning finite-dimensional representations that prioritize interpretability and expressivity.
