Statistical Model Checking for Stochastic Hybrid Systems
Alexandre David (Aalborg University), Dehui Du (East China Normal, University), Kim G. Larsen (Aalborg University), Axel Legay (INRIA Rennes),, Marius Miku\v{c}ionis (Aalborg University), Danny B{\o}gsted Poulsen (Aalborg, University), Sean Sedwards (INRIA Rennes)
TL;DR
This paper introduces new methods for statistical model checking of stochastic hybrid systems using UPPAAL-SMC, enabling analysis of complex systems in biology and energy management.
Contribution
It extends UPPAAL-SMC with race-based stochastic semantics for networks of hybrid systems and details its implementation and applications.
Findings
Successful application to systems biology models
Effective analysis of energy-aware building systems
Enhanced capabilities for stochastic hybrid system verification
Abstract
This paper presents novel extensions and applications of the UPPAAL-SMC model checker. The extensions allow for statistical model checking of stochastic hybrid systems. We show how our race-based stochastic semantics extends to networks of hybrid systems, and indicate the integration technique applied for implementing this semantics in the UPPAAL-SMC simulation engine. We report on two applications of the resulting tool-set coming from systems biology and energy aware buildings.
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