@inproceedings{6a3573a50e1f4db48fe35dcb85460405,
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).",
keywords = "Ordinary Differential Equations, Stochastic Symmetric Nets, Symbolic analysis, Symbolic structural techniques, Symmetries",
author = "Marco Beccuti and Lorenzo Capra and \{De Pierro\}, Massimiliano and Giuliana Franceschinis and Simone Pernice",
note = "Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 15th European Performance Engineering Workshop, EPEW 2018 ; Conference date: 29-10-2018 Through 30-10-2018",
year = "2018",
doi = "10.1007/978-3-030-02227-3\_3",
language = "English",
isbn = "9783030022266",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "30--45",
editor = "Anne Remke and Paolo Ballarini and Beno{\^i}t Barbot and Rena Bakhshi and Hind Castel-Taleb",
booktitle = "Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings",
address = "Germany",
}