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.
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks,
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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.