[2505.04997] Foam-Agent: Towards Automated Intelligent CFD Workflows

Computer Science > Artificial Intelligence arXiv:2505.04997 (cs) [Submitted on 8 May 2025 (v1), last revised 5 Mar 2026 (this version, v2)] Title:Foam-Agent: Towards Automated Intelligent CFD Workflows Authors:Ling Yue, Nithin Somasekharan, Tingwen Zhang, Yadi Cao, Zhangze Chen, Shimin Di, Shaowu Pan View a PDF of the paper titled Foam-Agent: Towards Automated Intelligent CFD Workflows, by Ling Yue and 6 other authors View PDF HTML (experimental) Abstract:Computational fluid dynamics (CFD) has been the main workhorse of computational physics. Yet its steep learning curve and fragmented, multi-stage workflow create significant barriers. To address these challenges, we present Foam-Agent, a multi-agent framework leveraging large language models (LLMs) to automate the end-to-end CFD workflow from a single natural language prompt. Foam-Agent orchestrates the comprehensive simulation workflow from mesh generation and high-performance computing job scripting to post-processing visualization. The system integrates retrieval-augmented generation with dependency-aware scheduling to synthesize high-fidelity simulation configurations. Furthermore, Foam-Agent adopts the Model Context Protocol to expose its core functions as discrete, callable tools. This allows for flexible integration and use by any other agentic systems. Evaluated on 110 simulation tasks, Foam-Agent achieved a state-of-the-art execution success rate of 88.2% without expert intervention. These results demonstrate how...