Unsafe probabilities and risk contours for stochastic processes using convex optimization
Year: 2026
Type: article
Abstract: When evaluating safety specifications for trajectories of a dynamical system, it is vital to be able to bound the worst-case probability of unsafety (constraint violation) Certifications of stochastic safety and worst-case probabilities of unsafety can be expressed as infinite-dimensional linear programs (e.g. stochastic barrier functions, occupation measure problems) This paper proves that the infinite-dimensional linear programs and their finite-dimensional Moment-Sum-of-Squares truncations are nonconservative (to the true probability of unsafety) under compactness and regularity conditions in stochastic dynamics. Unsafe-probability estimates and risk contours are generated for example stochastic processes.
Source: Automatica
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,760
Funders: National Science Foundation, U.S. Department of Homeland Security, Deutsche Forschungsgemeinschaft, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Eidgenössische Technische Hochschule Zürich, Stuttgart Center for Simulation Science, Universität Stuttgart, Office of Naval Research, Air Force Office of Scientific Research
Awards: G1294180184, G2855079135, G4300476733, Collaborative Research: Modeling and Control of Non-Passive Networks with Distributed Time-Delays: Application in Epidemic Control, CPS:Medium: Safe Learning-Enabled Cyberphysical Systems, G6562578720, EXC 2075: Datenintegrierte Simulationswissenschaft (SimTech)
OpenAlex ID: W7143969213