A tailored scenario optimization method yields tunable reachable-set estimates with a posteriori guarantees against adversarial noise and explicit degradation bounds under Wasserstein shifts.
On the ellipsoidal bounds of the reachable set for a class of time-delayed nonlinear systems with bounded input,
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Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees
A tailored scenario optimization method yields tunable reachable-set estimates with a posteriori guarantees against adversarial noise and explicit degradation bounds under Wasserstein shifts.