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Performance assessment
of complex systems


Next presentation of PyCATSHOO 
MMR 11 Hong Kong

JUN 3 - 7, 2019


Hybrid systems mix two kinds of behaviours:

 The discrete and stochastic behaviour which is in general due to failures and repairs of the system’s constituents.
 The continuous and deterministic physical phenomena which evolve inside the system.

Current conventional approaches to probabilistic safety assessments are not able to take into account both behaviours.

Because of the conservative assumptions supposed to remedy the lack of physical phenomena modelling, this usually leads to the loss of precious safety margins. 

PyCATSHOO solves this problem 

PyCATSHOO is based on the theoretical framework of Piecewise Deterministic Markov Processes. It implements this framework thanks to distributed hybrid stochastic automata and object oriented modeling.

Such an approach minimizes the surplus of complexity introduced by the hybrid behaviour of the systems.

PyCATSHOO is written in C++. It can be used on Windows, Linux and OSX and takes advantage of multicore architectures.

Both Python and C++ APIs are available. These APIs can be used either to model specific systems or for generic modelling i.e. for the creation of libraries of component models.