Compositional small-gain framework constructs augmented control barrier certificates from subsystem certificates to synthesize safety controllers and bound safety probability for networks of up to 1000 stochastic hybrid subsystems, reducing complexity to subsystem scale.
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Set-based training of neural barrier certificates uses a loss function that encodes all safety properties so that zero loss formally proves the certificate is valid, collapsing iterative training and verification into one procedure.
A physics-informed scenario approach selects data samples close to a physics model to reduce dataset size while constructing guaranteed barrier certificates for infinite-horizon safety of nonlinear systems.
citing papers explorer
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Compositional Design of Safety Controllers for Large-Scale Stochastic Hybrid Systems
Compositional small-gain framework constructs augmented control barrier certificates from subsystem certificates to synthesize safety controllers and bound safety probability for networks of up to 1000 stochastic hybrid subsystems, reducing complexity to subsystem scale.
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Set-Based Training of Neural Barrier Certificates for Safety Verification of Dynamical Systems
Set-based training of neural barrier certificates uses a loss function that encodes all safety properties so that zero loss formally proves the certificate is valid, collapsing iterative training and verification into one procedure.
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A Physics-Informed Scenario Approach with Data Mitigation for Safety Verification of Nonlinear Systems
A physics-informed scenario approach selects data samples close to a physics model to reduce dataset size while constructing guaranteed barrier certificates for infinite-horizon safety of nonlinear systems.