Toward AI-Agent-Driven Particle Transport Simulations: Implementation of AI-Assisted Workflows for PHITS

Abstract

Monte Carlo particle transport codes are powerful tools, but their use requires substantial knowledge of input preparation, execution, and result analysis. In this study, we present a code-side strategy for applying existing AI assistants and AI agents to PHITS. Two complementary sets of AI-ready resources were prepared from manuals, lecture materials, sample inputs, utility information, and developer-curated cautions: a bundled knowledge base for retrieval-augmented generation (RAG)-based assistants and a compact agent reference for direct use by AI agents. The knowledge base was loaded into NotebookLM to provide conversational PHITS support, while the agent reference was combined with PHITS-specific policies and execution rules to enable Codex and Claude Code to edit input files, execute calculations, inspect errors, analyze results, and assist with source-code modification and compilation. Five demonstration tasks covered input modification, repeated simulations, parameter optimization, program compilation, post-processing, and result interpretation. The results showed that AI agents could handle complex PHITS workflows when appropriate resources and rules were provided. Practical lessons included precise prompts, human verification, well-documented sample files, explicit execution policies, and command-line-accessible tools. These findings support bundling AI-ready resources with particle transport codes to enable the use of general-purpose AI tools without requiring dedicated code-specific applications.