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.
Data-driven control of nonlinear systems: Beyond polynomial dynamics
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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.