用大模型自动完成FLUKA蒙特卡洛模拟，省时省力少出错。
AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA
- 用LLM+AI代理自动改输入文件、跑模拟、处理结果
- 案例验证可处理微剂量学等通用与专用场景
- 适合高能物理、医学物理等领域研究人员使用
蒙特卡洛（MC）模拟，尤其是使用FLUKA，对科学和工程领域复现真实场景至关重要。尽管功能强大且通用，但FLUKA在自动化和与外部后处理工具集成方面存在显著局限，导致流程学习成本高、耗时且易出错。传统方法依赖Shell、Python脚本、MATLAB或Excel，需大量人工干预，缺乏灵活性，难以适应动态变化。本研究探索大型语言模型（LLMs）与AI代理在解决上述问题中的潜力。AI代理结合自然语言理解与自主推理能力，适用于自动化决策与自适应规划。我们提出AutoFLUKA，基于LangChain框架开发的AI代理应用，用于自动化典型FLUKA MC模拟工作流。AutoFLUKA可修改FLUKA输入文件、执行模拟并高效处理结果以实现可视化，显著减少人工劳动与错误。案例研究显示，该系统可应对通用及领域特定任务，如微剂量学，具备良好的可扩展性与灵活性。研究还表明，检索增强生成（RAG）工具可作为虚拟助手，进一步提升用户体验、效率与时间节省。结论：AutoFLUKA在自动化MC模拟工作流方面实现重要突破，提供可靠解决方案，不仅节省时间与资源，也为高能物理、医学物理、核工程、空间与环境科学的研究与开发开辟新范式。
原文摘要 · Abstract (English)
Monte Carlo (MC) simulations, particularly using FLUKA, are essential for replicating real-world scenarios across scientific and engineering fields. Despite the robustness and versatility, FLUKA faces significant limitations in automation and integration with external post-processing tools, leading to workflows with a steep learning curve, which are time-consuming and prone to human errors. Traditional methods involving the use of shell and Python scripts, MATLAB, and Microsoft Excel require extensive manual intervention and lack flexibility, adding complexity to evolving scenarios. This study explores the potential of Large Language Models (LLMs) and AI agents to address these limitations. AI agents, integrate natural language processing with autonomous reasoning for decision-making and adaptive planning, making them ideal for automation. We introduce AutoFLUKA, an AI agent application developed using the LangChain Python Framework to automate typical MC simulation workflows in FLUKA. AutoFLUKA can modify FLUKA input files, execute simulations, and efficiently process results for visualization, significantly reducing human labor and error. Our case studies demonstrate that AutoFLUKA can handle both generalized and domain-specific cases, such as Microdosimetry, with an streamlined automated workflow, showcasing its scalability and flexibility. The study also highlights the potential of Retrieval Augmentation Generation (RAG) tools to act as virtual assistants for FLUKA, further improving user experience, time and efficiency. In conclusion, AutoFLUKA represents a significant advancement in automating MC simulation workflows, offering a robust solution to the inherent limitations. This innovation not only saves time and resources but also opens new paradigms for research and development in high energy physics, medical physics, nuclear engineering space and environmental science.
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