CFDLLMBench: A Benchmark Suite for Evaluating Large Language Models in Computational Fluid Dynamics

ICLR 2026 Conference Submission19084 Authors

Keywords: computational fluid dynamics, CFD, numerical analysis, large language models, LLMs, benchmark, agent, openfoam

TL;DR: A holistic benchmark to evaluate how well large language models can understand, select, implement algorithms, and use tools of CFD to simulate fluid flows.

Abstract: Large Language Models (LLMs) have demonstrated strong performance across general NLP tasks, but their utility in automating numerical experiments of complex physical system, a critical and labor-intensive component, remains underexplored. As the major workhorse of computational science over the past decades, Computational Fluid Dynamics (CFD) offers a uniquely challenging testbed for evaluating the scientific capabilities of LLMs. We introduce CFDLLMBench, a benchmark suite comprising three complementary components, CFDQuery, CFDCodeBench, and FoamBench, designed to holistically evaluate LLM performance across three key competencies: graduate-level CFD knowledge, numerical and physical reasoning of CFD, and context-dependent implementation of CFD workflows. Grounded in real-world CFD practices, our benchmark combines a detailed task taxonomy with a rigorous evaluation framework to deliver reproducible results and quantify LLM performance across code executability, solution accuracy, and numerical convergence behavior. CFDLLMBench establishes a solid foundation for the development and evaluation of LLM-driven automation of numerical experiments for complex physical systems.

Primary Area: datasets and benchmarks

Submission Number: 19084