Software development for abstractions of stochastic hybrid systems
Stochastic hybrid systems (SHS) are dynamical models for the interaction of continuous and discrete states. The probabilistic evolution of continuous and discrete parts of the system are coupled, which makes analysis and verification of such systems compelling. Among specifications of SHS, probabilistic invariance and reach-avoid have received quite some attention recently. Numerical methods have been developed to compute these two specifications. These methods are mainly based on the state space partitioning and abstraction of SHS by Markov chains, which are optimal in the sense of reduction in abstraction error with minimum number of Markov states.
The goal of the project is to combine codes have been developed for these methods. The student should also design a nice user interface (for the choice of dynamical equations, parameters, and methods, etc.).
Courses: Probabilistic Model Checking, Probability and Computing, Numerical Solution of Differential Equations
Prerequisites: Some familiarity with stochastic processes, working knowledge of MATLAB and C