Learning computational fluid dynamics (CFD) traditionally relies on computationally intensive simulations of the Navier-Stokes equations. Recently, large language models (LLMs) have shown remarkable pattern recognition and reasoning abilities in natural language processing (NLP) and computer vision (CV). However, these models struggle with the complex geometries inherent in fluid dynamics. We introduce FLUID-LLM, a novel framework combining pre-trained LLMs with spatiotemporal-aware encoding to predict unsteady fluid dynamics. Our approach leverages the temporal autoregressive abilities of LLMs alongside spatial-aware layers, bridging the gap between previous CFD prediction methods. Evaluations on standard benchmarks reveal significant performance improvements across various fluid datasets. Our results demonstrate that FLUID-LLM effectively integrates spatiotemporal information into pre-trained LLMs, enhancing CFD task performance.

翻译：传统计算流体动力学（CFD）的学习依赖于对纳维-斯托克斯方程进行高计算量的数值模拟。近年来，大语言模型（LLMs）在自然语言处理（NLP）和计算机视觉（CV）领域展现出卓越的模式识别与推理能力。然而，这类模型在处理流体动力学中固有的复杂几何构型时仍存在困难。我们提出FLUID-LLM这一创新框架，将预训练大语言模型与时空感知编码相结合，用于预测非定常流体动力学行为。该方法利用大语言模型的时间自回归能力与空间感知层协同作用，弥合了现有CFD预测方法之间的鸿沟。在标准基准测试上的评估表明，该方法在多种流体数据集上均取得了显著的性能提升。实验结果显示，FLUID-LLM能有效将时空信息融入预训练大语言模型，从而增强CFD任务的执行效果。