FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models

• URL: http://arxiv.org/abs/2406.04501v1
• Date: Thu, 6 Jun 2024 20:55:40 GMT
• Title: FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models
• Authors: Max Zhu, Adrián Bazaga, Pietro Liò,
• Abstract summary: Large language models (LLMs) have shown remarkable pattern recognition and reasoning abilities. We introduce FLUID-LLM, a novel framework combining pre-trained LLMs with pre-aware encoding to predict unsteady fluid dynamics. Our results demonstrate that FLUID-LLM effectively integratestemporal information into pre-trained LLMs, enhancing CFD task performance.
• Score: 15.964726158869777
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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