@article{BeccEPEW18,
author={Beccuti,M. and Capra,L. and De Pierro,M. and Franceschinis,G. and Pernice,S. },
year={2018},
title={Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding},
abstract={This paper concerns the quantitative evaluation of Stochastic Symmetric Nets (SSN) by means of a fluid approximation technique particularly suited to analyse systems with a huge state space. In particular a new efficient approach is proposed to derive the deterministic process approximating the original stochastic process through a system of Ordinary Differential Equations (ODE). The intrinsic symmetry of SSN models is exploited to significantly reduce the size of the ODE system while a symbolic calculus operating on the SSN arc functions is employed to derive such system efficiently, avoiding the complete unfolding of the SSN model into a Stochastic Petri Net (SPN). },
series={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) },
volume={11178 LNCS},
pages={30-45},
language={English},
}