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.

通过将预训练大型语言模型（LLM）与时空感知编码相结合，FLUID-LLM 能够更好地整合时空信息，提高计算流体动力学（CFD）任务的性能。