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Net Learning.

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    This study introduces net learning (NL), combining Petri net (PN) modeling with graph learning for large-scale systems. NL effectively analyzes stochastic PNs by extracting hidden features, enhancing system performance analysis.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Systems Engineering

    Background:

    • Graph neural networks excel in graph-structured data but are less suited for dynamic systems.
    • Petri nets (PNs) model event-driven systems but struggle with large-scale data analysis.
    • Existing methods limit the applicability of PNs in complex, real-world scenarios.

    Purpose of the Study:

    • To propose a novel framework, net learning (NL), integrating PN modeling with graph learning.
    • To address the challenge of large-scale data analysis in Petri net applications.
    • To develop effective algorithms for performance analysis of stochastic PNs using NL.

    Main Methods:

    • Developed a net learning (NL) framework combining Petri net (PN) modeling and graph learning computation.
    • Designed two NL algorithms for performance analysis of stochastic PNs.
    • Mapped net information to a low-dimensional feature space to extract hidden features.

    Main Results:

    • The proposed net learning framework effectively integrates PN modeling with graph learning.
    • The developed NL algorithms successfully extract hidden feature information from PNs.
    • Experimental results validate the effectiveness of NL for stochastic PN performance analysis.

    Conclusions:

    • Net learning (NL) offers a powerful approach to overcome limitations in large-scale Petri net analysis.
    • The proposed NL algorithms enhance the performance analysis of stochastic PNs.
    • This work advances the applicability of Petri nets in complex, dynamic systems.