Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics

TL;DR

This paper introduces a new scaling law for AI models in Computational Fluid Dynamics, highlighting the importance of high-fidelity data and providing cost estimates for developing foundation models in this complex field.

Contribution

It proposes the first scaling law incorporating CFD inputs, addressing challenges in data generation and model training for fluid dynamics AI models.

Findings

Quantitative estimates for large-scale limits of CFD AI models

Identification of regimes dominated by data generation or model training costs

Cost and time estimates for building CFD foundation models

Abstract

Driven by the advancement of GPUs and AI, the field of Computational Fluid Dynamics (CFD) is undergoing significant transformations. This paper bridges the gap between the machine learning and CFD communities by deconstructing industrial-scale CFD simulations into their core components. Our main contribution is to propose the first scaling law that incorporates CFD inputs for both data generation and model training to outline the unique challenges of developing and deploying these next-generation AI models for complex fluid dynamics problems. Using our new scaling law, we establish quantitative estimates for the large-scale limit, distinguishing between regimes where the cost of data generation is the dominant factor in total compute versus where the cost of model training prevails. We conclude that the incorporation of high-fidelity transient data provides the optimum route to a foundation model. We constrain our theory with concrete numbers, providing the first public estimates on the computational cost and time to build a foundation model for CFD.