Constructs k-inductive neural barrier certificates for partially unknown nonlinear dynamics by combining neural networks, a data-driven fundamental lemma from one trajectory, and CEGIS-SMT verification.
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Dominance functions from a small number of trajectories serve as dissipative and expressive building blocks for formal safety certificates in monotone discrete-time systems.
HyperCertificates combine closure certificates for lookahead with barrier and ranking functions to verify discrete-time systems against HyperLTL specifications.
citing papers explorer
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k-Inductive Neural Barrier Certificates for Unknown Nonlinear Dynamics
Constructs k-inductive neural barrier certificates for partially unknown nonlinear dynamics by combining neural networks, a data-driven fundamental lemma from one trajectory, and CEGIS-SMT verification.
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Trajectory-based Safety of Monotone Systems: Verification and Control Synthesis
Dominance functions from a small number of trajectories serve as dissipative and expressive building blocks for formal safety certificates in monotone discrete-time systems.
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HyperCertificates: Verification of Discrete-time Dynamical Systems against HyperLTL Specifications
HyperCertificates combine closure certificates for lookahead with barrier and ranking functions to verify discrete-time systems against HyperLTL specifications.