A kernel method computes Koopman eigenfunctions that preserve the Jacobian spectrum for Lyapunov-based stability analysis of nonlinear dynamical systems.
Physics-informed neural network Lyapunov functions: PDE characterization, learning, and verification
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Zubov-Net aligns prescribed regions of attraction defined by learnable Lyapunov functions with true regions in Neural ODEs via a differentiable Zubov consistency loss, claiming to reconcile accuracy and certified robustness.
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RKHS method for computing Koopman-based Lyapunov functions
A kernel method computes Koopman eigenfunctions that preserve the Jacobian spectrum for Lyapunov-based stability analysis of nonlinear dynamical systems.
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Learning Aligned Stability in Neural ODEs Reconciling Accuracy with Robustness
Zubov-Net aligns prescribed regions of attraction defined by learnable Lyapunov functions with true regions in Neural ODEs via a differentiable Zubov consistency loss, claiming to reconcile accuracy and certified robustness.