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Bootstrapping SysML with LLM-based inference and integrated discrete-event simulation

Abdurrahman Alshareef

SIMULATION: Transactions of The Society for Modeling and Simulation International2026https://doi.org/10.1177/00375497261434182article
AJG 1
Weight
0.50

What the paper says

The demand for systems engineering methodologies with integrative artificial intelligence (AI) has been increasing. Model-based systems engineering offers a disciplined, structured methodology. However, it encounters difficulties with semantic interpretation and domain adaptation, especially across different contexts. In this work, we examine the potential of generative AI to address this challenge. We implement a dual approach to enrich the modeling experience by incorporating domain adaptations via large language models and executable semantics via discrete-event simulation. The result is a bootstrapped, end-to-end automated system model construction from minimal entry points, featuring built-in, generic executable capability that adheres to the simulation-to-production system principle. We will demonstrate how a user of such an approach can produce a sound, semantically rich model with advanced simulations from a minimal textual entry. We also discuss mechanisms for incorporating knowledge and expertise through a convenient yet effective human-in-the-loop integration. We demonstrate the approach through a detailed semiconductor wafer fabrication experiment and further illustrate its generality across diverse domains through extensive generative and simulation-based evaluations.

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https://doi.org/https://doi.org/10.1177/00375497261434182

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@article{abdurrahman2026,
  title        = {{Bootstrapping SysML with LLM-based inference and integrated discrete-event simulation}},
  author       = {Abdurrahman Alshareef},
  journal      = {SIMULATION: Transactions of The Society for Modeling and Simulation International},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/00375497261434182},
}

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Bootstrapping SysML with LLM-based inference and integrated discrete-event simulation

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.