TY - GEN
T1 - A tool for the automatic derivation of symbolic ode from symmetric net models
AU - Beccuti, Marco
AU - Capra, Lorenzo
AU - De Pierro, Massimiliano
AU - Franceschinis, Giuliana
AU - Follia, Laura
AU - Pernice, Simone
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - High-level Petri nets (HLPNs) are an expressive formalism well supported by a number of tools that automate the editing and the interactive simulation of models and some kinds of analytical techniques, mainly based on state-space exploration. Structural analysis of HLPNs is, however, a challenging task not yet adequately supported and it is often accomplished via the unfolding of an HLPN into a corresponding low-level Petri Net. An approach to derive a system of Ordinary Differential Equations (ODEs) from a Stochastic Symmetric Net (SSN) has been proposed a few years ago, based on the net's unfolding and subsequent grouping of similar equations. This method has been recently improved by providing an algorithm that directly derives a compact ODE system (from a partially unfolded net) in a symbolic way, through algebraic manipulation of SSN annotations. In this paper, we present the automation of the calculus of Symbolic ODEs (SODEs) for SSN models as a new module of SNexpression, a tool for the symbolic structural analysis of Symmetric Nets. An application of the tool/technique to a variant of a SIRS epidemic model including antibiotic resistance is also described.
AB - High-level Petri nets (HLPNs) are an expressive formalism well supported by a number of tools that automate the editing and the interactive simulation of models and some kinds of analytical techniques, mainly based on state-space exploration. Structural analysis of HLPNs is, however, a challenging task not yet adequately supported and it is often accomplished via the unfolding of an HLPN into a corresponding low-level Petri Net. An approach to derive a system of Ordinary Differential Equations (ODEs) from a Stochastic Symmetric Net (SSN) has been proposed a few years ago, based on the net's unfolding and subsequent grouping of similar equations. This method has been recently improved by providing an algorithm that directly derives a compact ODE system (from a partially unfolded net) in a symbolic way, through algebraic manipulation of SSN annotations. In this paper, we present the automation of the calculus of Symbolic ODEs (SODEs) for SSN models as a new module of SNexpression, a tool for the symbolic structural analysis of Symmetric Nets. An application of the tool/technique to a variant of a SIRS epidemic model including antibiotic resistance is also described.
KW - High-Level Petri Nets
KW - Ordinary Differential Equations
KW - Symbolic structural relations
KW - Symmetric Nets
UR - http://www.scopus.com/inward/record.url?scp=85077789968&partnerID=8YFLogxK
U2 - 10.1109/MASCOTS.2019.00015
DO - 10.1109/MASCOTS.2019.00015
M3 - Conference contribution
AN - SCOPUS:85077789968
T3 - Proceedings - IEEE Computer Society's Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, MASCOTS
SP - 36
EP - 48
BT - Proceedings - 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2019
PB - IEEE Computer Society
T2 - 27th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, MASCOTS 2019
Y2 - 22 October 2019 through 25 October 2019
ER -