State-Space Kolmogorov-Arnold Networks fit a linear state-space model plus sparse learnable univariate functions, and the shapes of those functions recover the known cubic and saturation nonlinearities in two benchmark systems.
Retrieving highly structured models starting from black-box nonlinear state-space models using polynomial decoupling,
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State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification
State-Space Kolmogorov-Arnold Networks fit a linear state-space model plus sparse learnable univariate functions, and the shapes of those functions recover the known cubic and saturation nonlinearities in two benchmark systems.