SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions.
Discovering governing equations from data by sparse identification of nonlinear dynamical systems,
3 Pith papers cite this work. Polarity classification is still indexing.
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SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.
A weak-form regression framework using spatial Gaussian kernels removes bias in recovering drift b(x) and diffusion a(x) for stochastic generators from single sparse regressions, validated on benchmarks with low coefficient and density errors.
citing papers explorer
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Stable Fiber-Koopman Residual Dynamics for Environment-Constrained Robust Control
SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions.
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Adaptive Control with Sparse Identification of Nonlinear Dynamics
SP-ICL integrates L1 regularization with integral concurrent learning using sliding modes to recover sparse parameters online and proves ultimate boundedness of closed-loop trajectories via non-smooth Lyapunov analysis.
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Weak-Form Recovery of Stochastic Generators and Dynamical Invariants
A weak-form regression framework using spatial Gaussian kernels removes bias in recovering drift b(x) and diffusion a(x) for stochastic generators from single sparse regressions, validated on benchmarks with low coefficient and density errors.