A framework learns fault-indexed Perron-Frobenius operators from trajectory data to provide certifiable 2-Wasserstein bounds for detecting actuator faults and enabling recovery via density propagation in nonlinear control-affine systems.
Probabilistic reachability of stochas- tic control systems: A contraction-based approach
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State-dependent conformal prediction with genetic-algorithm state partitioning and branch-merging reachability produces tighter high-confidence perception-error bounds for scalable verification of neurally controlled autonomous systems.
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Perron-Frobenius Contractive Operator Matching for Data-Driven Reachable Fault Identification and Recovery
A framework learns fault-indexed Perron-Frobenius operators from trajectory data to provide certifiable 2-Wasserstein bounds for detecting actuator faults and enabling recovery via density propagation in nonlinear control-affine systems.
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Statistical-Symbolic Verification of Perception-Based Autonomous Systems using State-Dependent Conformal Prediction
State-dependent conformal prediction with genetic-algorithm state partitioning and branch-merging reachability produces tighter high-confidence perception-error bounds for scalable verification of neurally controlled autonomous systems.