A Digital Twin Modeling Code Generation Framework based on Large Language Model

Digital twin has made significant achievements in applications across various fields by bridging the gap between virtual and physical world. However, the modeling of digital twin faces challenges of labor-intensive and time-consuming manual modeling or coding. With the emergence of artificial intelligence foundation models, the integration of large language models (LLMs) and digital twin may contribute to enhancing modeling efficiency significantly. Therefore, in this paper, a framework of digital twin modeling code generation is proposed based on LLMs. Given instructions in natural language prompts, modeling code in digital twin software will be generated from LLMs. Firstly, a tree diagram-based code generation method is proposed to generate main section of modeling code. Additionally, a randomized code completion method is proposed to complete missing parts which are unspecified or unclear in prompts. Finally, a case study is given in NVIDIA Omniverse for a digital twin construction based on framework proposed in this paper.