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.
Learning for Dynamics and Control Conference , pages=
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Dominance functions from a small number of trajectories serve as dissipative and expressive building blocks for formal safety certificates in monotone discrete-time systems.
Safety certification of dynamical systems is reformulated as direct classification via kernel embeddings on trajectories, bypassing recursive DP to avoid error compounding and support non-Markovian dynamics.
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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Safety Certification is Classification
Safety certification of dynamical systems is reformulated as direct classification via kernel embeddings on trajectories, bypassing recursive DP to avoid error compounding and support non-Markovian dynamics.