# Integrating large language models with digital twins: supporting modelling and design of sustainable systems

Kunal Suri

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## To cite this version:

Kunal Suri. Integrating large language models with digital twins: supporting modelling and design of sustainable systems. 2024. cea-04698306

HAL Id: cea-04698306

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Preprint submitted on 15 Sep 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# Integrating Large Language Models with Digital
Twins: Supporting Modelling and Design of
Sustainable Systems

Kunal Suri¹[0000-0002-2341-5343]

Université Paris-Saclay, CEA, List, F-91120, Palaiseau, France

kunal.suri@cea.fr

**Abstract.** The integration of Large Language Models (LLMs) and Digital Twins (i.e., virtual models that replicate physical systems) offer new opportunities to support business process analysts by (semi)automating process discovery and (re)design steps in the Business Process Management (BPM) lifecycle. These steps are the starting points of developing any sustainable Process-Aware Information System (PAIS). Moreover, these steps are generally tedious and error-prone, but the advancements in the domain of Generative AI, especially LLMs, such as ChatGPT, Llama, and Mistral, show potential. This is because LLMs have significantly become better in understanding the semantics and context present in large volumes of unstructured data such as documents, emails, maintenance logs, operational videos, and chat exchanges. Thus, the integration of LLMs with Digital Twins supports a more adaptive and context-aware system design capability that can handle real-time updates and dynamically generate or refine the process models. In other words, it results in context-aware, responsive process (re)designs that will reduce the time spent on manual analysis and improve the accuracy of process (re)discovery. However, several challenges remain, primarily, managing the quality, accuracy and bias of LLM-generated outputs, and ensuring that the generated process models adhere to requirements and real-world constraints.

Despite these challenges, integrating LLMs and Digital Twins will support the advancement of traditional modelling techniques, which is needed in several fast-evolving sectors such as manufacturing, healthcare, and smart cities. This is mainly because LLM-augmented Digital Twins will accelerate efficiency by enabling dynamic, domain-specific and data-driven optimizations over both structured and unstructured data along with supporting automated decision-making. This study explores both the advantages and limitations of combining LLMs and Digital Twins, along with the technological foundations and practical implications of this integration.

**Keywords:** Large Language Models · Digital Twins · Information Systems · Process Modelling · BPMN · Model-Based Systems Engineering · Sustainability