AI 中文总结
研究针对多物理场模拟计算需求及训练数据集瓶颈，提出基于模型上下文协议的Agents4GEOS智能体框架，借助52个领域感知工具及协调器，自动化日常任务，助力专家专注工作挑战。
AI 中文摘要
多物理场模拟对于理解和监测复杂的地下过程（如二氧化碳储存）至关重要。其计算需求催生了替代模型，对于非结构化网格，图神经网络是理想选择。开发中的主要瓶颈是生成和管理训练所需的大型物理一致模拟数据集。为应对这一挑战，我们提出了Agents4GEOS，这是一个基于模型上下文协议（MCP）构建的人工智能智能体框架，为使用开源多物理场模拟器GEOS的自然语言驱动工作流程提供52个领域感知工具。该智能体便于输入文件创建、网格检查、流体属性计算和结果后处理。通过人工策划的技能和由协调器协调的新上下文子智能体，系统执行复杂工作流程，评估模拟输出，诊断问题并提出改进建议，将每个量基于实际计算。通过自动化日常任务，Agents4GEOS使领域专家能够专注于工作中最具挑战性的方面。
英文摘要
Multi-physics simulations are essential for understanding and monitoring intricate subsurface processes such as CO2 storage. Their computational demands call for surrogate models and, for unstructured meshes, Graph Neural Networks (GNNs) are natural candidates. The main bottleneck in developing them is generating and managing the large, physically consistent simulation datasets required for training. To address this challenge, we present Agents4GEOS, an AI-agent framework built on the Model Context Protocol (MCP) that provides 52 domain-aware tools for natural-language-driven workflows with GEOS, an open-source multi-physics simulator. The agent facilitates input-file creation, mesh inspection, fluid-property computation, and result post-processing. Through human-curated skills and fresh-context subagents coordinated by an orchestrator, the system executes complex workflows, evaluates simulation outputs, diagnoses issues, and suggests improvements, grounding every quantity in actual computation. By automating routine tasks, Agents4GEOS allows domain experts to focus on the most challenging aspects of their work.
Comments14 pages, 11 Figures
