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Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. By leveraging principles of Dynamic Data-Driven Applications Systems (DDDAS), DDTs can formulate computational modalities for feedback loops, model updates and decision-making, including autonomous ones. However, understanding autonomous decision-making often requires technical and domain-specific knowledge. This paper explores using large language models (LLMs) to provide an explainability platform for DDTs, generating natural language explanations of the system’s decision-making by leveraging domain-specific knowledge bases. A case study from smart agriculture is presented.

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Large Language Models for Explainable Decisions in Dynamic Digital Twins

  • 会议论文

  • 发表日期：

    2026年

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  • Nan Zhang,
  • Christian Vergara-Marcillo,
  • Georgios Diamantopoulos,
  • Jingran Shen,
  • Nikos Tziritas,
  • Rami Bahsoon,
  • Georgios Theodoropoulos

摘要

Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. By leveraging principles of Dynamic Data-Driven Applications Systems (DDDAS), DDTs can formulate computational modalities for feedback loops, model updates and decision-making, including autonomous ones. However, understanding autonomous decision-making often requires technical and domain-specific knowledge. This paper explores using large language models (LLMs) to provide an explainability platform for DDTs, generating natural language explanations of the system’s decision-making by leveraging domain-specific knowledge bases. A case study from smart agriculture is presented.