Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding

Marco Beccuti; Lorenzo Capra; Massimiliano De Pierro; Giuliana Franceschinis; Simone Pernice

doi:10.1007/978-3-030-02227-3_3

Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding

Marco Beccuti, Lorenzo Capra, Massimiliano De Pierro, Giuliana Franceschinis, Simone Pernice

Dipartimento di Scienze e Innovazione Tecnologica

Risultato della ricerca: Capitolo in libro/report/atti di convegno › Contributo a conferenza › peer review

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).

Lingua originale	Inglese
Titolo della pubblicazione ospite	Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings
Editor	Anne Remke, Paolo Ballarini, Benoît Barbot, Rena Bakhshi, Hind Castel-Taleb
Editore	Springer Verlag
Pagine	30-45
Numero di pagine	16
ISBN (stampa)	9783030022266
DOI	https://doi.org/10.1007/978-3-030-02227-3_3
Stato di pubblicazione	Pubblicato - 2018
Evento	15th European Performance Engineering Workshop, EPEW 2018 - Paris, France Durata: 29 ott 2018 → 30 ott 2018

Serie di pubblicazioni

Nome	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11178 LNCS
ISSN (stampa)	0302-9743
ISSN (elettronico)	1611-3349

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???event.eventtypes.event.conference???	15th European Performance Engineering Workshop, EPEW 2018
Paese/Territorio	France
Città	Paris
Periodo	29/10/18 → 30/10/18

Accesso al documento

10.1007/978-3-030-02227-3_3

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Beccuti, M., Capra, L., De Pierro, M., Franceschinis, G., & Pernice, S. (2018). Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding. In A. Remke, P. Ballarini, B. Barbot, R. Bakhshi, & H. Castel-Taleb (Edd.), Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings (pagg. 30-45). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11178 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-02227-3_3

Beccuti, Marco ; Capra, Lorenzo ; De Pierro, Massimiliano et al. / Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding. Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings. curatore / Anne Remke ; Paolo Ballarini ; Benoît Barbot ; Rena Bakhshi ; Hind Castel-Taleb. Springer Verlag, 2018. pagg. 30-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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Beccuti, M, Capra, L, De Pierro, M, Franceschinis, G & Pernice, S 2018, Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding. in A Remke, P Ballarini, B Barbot, R Bakhshi & H Castel-Taleb (edd.), Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11178 LNCS, Springer Verlag, pagg. 30-45, 15th European Performance Engineering Workshop, EPEW 2018, Paris, France, 29/10/18. https://doi.org/10.1007/978-3-030-02227-3_3

Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding. / Beccuti, Marco; Capra, Lorenzo; De Pierro, Massimiliano et al.
Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings. a cura di / Anne Remke; Paolo Ballarini; Benoît Barbot; Rena Bakhshi; Hind Castel-Taleb. Springer Verlag, 2018. pag. 30-45 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11178 LNCS).

Risultato della ricerca: Capitolo in libro/report/atti di convegno › Contributo a conferenza › peer review

TY - GEN

T1 - Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding

AU - Beccuti, Marco

AU - Capra, Lorenzo

AU - De Pierro, Massimiliano

AU - Franceschinis, Giuliana

AU - Pernice, Simone

PY - 2018

Y1 - 2018

N2 - 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).

AB - 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).

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KW - Stochastic Symmetric Nets

KW - Symbolic analysis

KW - Symbolic structural techniques

KW - Symmetries

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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T2 - 15th European Performance Engineering Workshop, EPEW 2018

Y2 - 29 October 2018 through 30 October 2018

ER -

Beccuti M, Capra L, De Pierro M, Franceschinis G, Pernice S. Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding. In Remke A, Ballarini P, Barbot B, Bakhshi R, Castel-Taleb H, editor, Computer Performance Engineering - 15th European Workshop, EPEW 2018, Proceedings. Springer Verlag. 2018. pag. 30-45. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-02227-3_3

Deriving Symbolic Ordinary Differential Equations from Stochastic Symmetric Nets Without Unfolding

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