AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA

• URL: http://arxiv.org/abs/2410.15222v1
• Date: Sat, 19 Oct 2024 21:50:11 GMT
• Title: AutoFLUKA: A Large Language Model Based Framework for Automating Monte Carlo Simulations in FLUKA
• Authors: Zavier Ndum Ndum, Jian Tao, John Ford, Yang Liu,
• Abstract summary: Monte Carlo (MC) simulations 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. This study explores the potential of Large Language Models (LLMs) and AI agents to address these limitations. We introduce AutoFLUKA, an AI agent application developed using the LangChain Python Framework to automate typical MC simulation in FLUKA.
• Score: 6.571041942559539
• License:
• Abstract: 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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