Symplectic inductive bias combined with chain policies yields sufficient conditions for target reachability in Hamiltonian systems whose sample complexity depends on recurrence and geometry rather than ambient dimension.
Formulas for data-driven control: Stabilization, optimality, and robustness
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.OC 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Sequences of SDPs jointly produce online stabilizing controllers and Lyapunov certificates for nonlinear systems while certifying recursive feasibility and estimating the region of attraction.
citing papers explorer
-
Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems
Symplectic inductive bias combined with chain policies yields sufficient conditions for target reachability in Hamiltonian systems whose sample complexity depends on recurrence and geometry rather than ambient dimension.
-
Semi-definite programs for online control of nonlinear systems with stability guarantees
Sequences of SDPs jointly produce online stabilizing controllers and Lyapunov certificates for nonlinear systems while certifying recursive feasibility and estimating the region of attraction.