A constrained symbolic regression method on expression trees discovers Lyapunov functions for autonomous dynamical systems without assuming their functional form.
Koopman-basedestimationofLyapunovfunctions: Theoryonareproducing kernelHilbertspace
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The paper develops three convex learning settings for hybrid models that enforce interpretability via reference regularization, subspace restrictions, and nonlinear manifold restrictions, re-parameterized through lifted operator features as kernel mixtures of interpretable components.
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A constrained symbolic regression approach for Lyapunov function discovery
A constrained symbolic regression method on expression trees discovers Lyapunov functions for autonomous dynamical systems without assuming their functional form.
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Convex Hybrid Modeling: An Operator-Based Approach
The paper develops three convex learning settings for hybrid models that enforce interpretability via reference regularization, subspace restrictions, and nonlinear manifold restrictions, re-parameterized through lifted operator features as kernel mixtures of interpretable components.