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This paper introduces significant advancements in the GreatMod modeling framework, enhancing its capacity to simulate systems characterized by non-Markovian dynamics accurately. These enhancements include the definition of a novel graphical formalism tailored to represent such complex models, alongside an extension of the Stochastic Simulation Algorithm to accommodate their simulation efficiently. Moreover, we validate the robustness of these improvements through two case studies: the Susceptible-Infected-Recovered model and the Parallel-Producer-Consumer model.

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Extension of the GreatMod Modeling Framework to Simulate Non-Markovian Processes with General-Distributed Events

  • 会议论文

  • 发表日期：

    2025年

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  • Irene Terrone,
  • Daniela Volpatto,
  • Simone Pernice,
  • Elvio Amparore,
  • Roberta Sirovich,
  • Francesca Cordero,
  • Marco Beccuti

摘要

This paper introduces significant advancements in the GreatMod modeling framework, enhancing its capacity to simulate systems characterized by non-Markovian dynamics accurately. These enhancements include the definition of a novel graphical formalism tailored to represent such complex models, alongside an extension of the Stochastic Simulation Algorithm to accommodate their simulation efficiently. Moreover, we validate the robustness of these improvements through two case studies: the Susceptible-Infected-Recovered model and the Parallel-Producer-Consumer model.