Physics-based simulation underpins engineering analysis but remains difficult to deploy in practice due to complex setup, parameterization, and interpretation. While Large Language Model-based agentic systems have shown promise in automating engineering computing workflows, they have primarily targeted structured, mesh-based problems. We present the first agentic AI workflow for meshless simulation in computational mechanics, demonstrated on debris flow modeling using Smoothed Particle Hydrodynamics (SPH) with the software DualSPHysics. By integrating tool orchestration, multimodal inputs (text and sketches), and human-in-the-loop interaction, the framework enables end-to-end simulation workflows for a class of problems that are inherently less structured and more challenging to automate. Results show that multimodal inputs not only enhance user experience but also reduces failure modes over text-only descriptions. Human-in-the-loop is critical for resolving ambiguities and handling SPH-specific configurations. We further introduce a cognitive-task-based evaluation of post-processing, showing strong performance in visualization and data extraction, with remaining gaps in higher-level SPH-specific physical reasoning that are amenable to improvement through domain-aware modeling. These results establish the viability of agentic AI for particle-based simulation and underscore its potential to transform the accessibility and efficiency of computational mechanics workflows.

翻译：基于物理的模拟是工程分析的基石，但由于其复杂的设置、参数化及结果解读，实际部署仍面临困难。尽管基于大型语言模型的智能代理系统在自动化工程计算工作流方面展现出潜力，但此前主要针对结构化网格类问题。本文首次提出计算力学中面向无网格模拟的智能代理AI工作流，通过光滑粒子流体动力学（SPH）方法及DualSPHysics软件，将其应用于泥石流建模。该框架集成工具编排、多模态输入（文本与草图）及人机交互闭环，能够对本质上更缺乏结构、更难以自动化的一类问题实现端到端模拟工作流。结果表明，多模态输入不仅提升了用户体验，还比纯文本描述减少了故障模式。人机交互对解决歧义及处理SPH特有配置至关重要。我们进一步引入基于认知任务的后处理评估方法，在可视化与数据提取方面展现出强劲性能，但在高层级SPH特定物理推理方面仍存在改进空间，可通过领域感知建模加以优化。这些结果证实了智能代理AI在粒子模拟中的可行性，并凸显其变革计算力学工作流可及性与效率的潜力。