ChronoLLM：基于 PyChrono 定制大语言模型以实现数字孪生泛化的框架
ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono
AI总结：
本文提出 ChronoLlama 框架，将面向代码生成定制的开源 LLM 与 PyChrono 结合，以自动生成多物理场仿真脚本并提升数字孪生仿真的速度、准确性与效率。
摘要：
近来，先进仿真技术与人工智能（AI）的融合正在革新科学与工程研究。ChronoLlama 提出了一个新颖框架，用于定制开源 LLM，专门面向代码生成，并与 PyChrono 配合进行多物理场仿真。这种集成旨在自动化并改进仿真脚本的创建，从而提升模型的准确性和效率。该组合利用 AI 驱动代码生成的速度以及基于物理的仿真的可靠性，为研究人员和工程师提供了强大工具。实证结果表明，仿真设置速度、生成代码的准确性以及整体计算效率均得到显著提升。ChronoLlama 不仅加快了多体系统的开发与测试，还开创了一种可扩展的、由 AI 增强的方法来管理复杂机械仿真。这种前沿 AI 与传统仿真平台的开创性集成，标志着工程应用中设计流程自动化与优化的一次重大飞跃。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
