ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915094439395328 |
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| author | Wang, Jingquan Zhang, Harry Slaton, Khailanii Wang, Shu Serban, Radu Wu, Jinlong Negrut, Dan |
| author_facet | Wang, Jingquan Zhang, Harry Slaton, Khailanii Wang, Shu Serban, Radu Wu, Jinlong Negrut, Dan |
| contents | Recently, the integration of advanced simulation technologies with artificial intelligence (AI) is revolutionizing science and engineering research. ChronoLlama introduces a novel framework that customizes the open-source LLMs, specifically for code generation, paired with PyChrono for multi-physics simulations. This integration aims to automate and improve the creation of simulation scripts, thus enhancing model accuracy and efficiency. This combination harnesses the speed of AI-driven code generation with the reliability of physics-based simulations, providing a powerful tool for researchers and engineers. Empirical results indicate substantial enhancements in simulation setup speed, accuracy of the generated codes, and overall computational efficiency. ChronoLlama not only expedites the development and testing of multibody systems but also spearheads a scalable, AI-enhanced approach to managing intricate mechanical simulations. This pioneering integration of cutting-edge AI with traditional simulation platforms represents a significant leap forward in automating and optimizing design processes in engineering applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_04062 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono Wang, Jingquan Zhang, Harry Slaton, Khailanii Wang, Shu Serban, Radu Wu, Jinlong Negrut, Dan Software Engineering Artificial Intelligence Computational Engineering, Finance, and Science Recently, the integration of advanced simulation technologies with artificial intelligence (AI) is revolutionizing science and engineering research. ChronoLlama introduces a novel framework that customizes the open-source LLMs, specifically for code generation, paired with PyChrono for multi-physics simulations. This integration aims to automate and improve the creation of simulation scripts, thus enhancing model accuracy and efficiency. This combination harnesses the speed of AI-driven code generation with the reliability of physics-based simulations, providing a powerful tool for researchers and engineers. Empirical results indicate substantial enhancements in simulation setup speed, accuracy of the generated codes, and overall computational efficiency. ChronoLlama not only expedites the development and testing of multibody systems but also spearheads a scalable, AI-enhanced approach to managing intricate mechanical simulations. This pioneering integration of cutting-edge AI with traditional simulation platforms represents a significant leap forward in automating and optimizing design processes in engineering applications. |
| title | ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono |
| topic | Software Engineering Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2501.04062 |