🤖 AI Summary
This work addresses the high computational cost of traditional computational fluid dynamics (CFD), which hinders rapid exploration in indoor environmental optimization. While existing generative surrogate models can capture complex flow fields, they suffer from inefficient iterative sampling. To overcome this limitation, the study introduces, for the first time, a drifting generative framework into fluid dynamics, proposing label-conditional and spatially conditional variants that enable single-pass forward generation in the latent space of a variational autoencoder (VAE). A label-aware masking mechanism is incorporated to enforce boundary condition consistency. The method achieves flow field accuracy and physical fidelity comparable to iterative diffusion models while accelerating inference by two orders of magnitude. Moreover, it generalizes effectively to unseen geometries, establishing an efficient new paradigm for real-time CFD surrogate modeling.
📝 Abstract
While Computational Fluid Dynamics (CFD) provides high-fidelity flow fields for optimizing indoor environments, its computational cost limits rapid exploration. To solve this problem generative surrogates offer better distribution modeling than deterministic networks, but iterative sampling is slow. To enable high-quality, single-pass generation, we adapt the novel generative drifting framework to fluid mechanics. We introduce a conditional architecture that performs drifting in a learned VAE latent space and uses label-aware masking to align generated samples with their boundary conditions. Our label-conditioned model matches iterative diffusion in accuracy and flow consistency while running two orders of magnitude faster. Additionally, we propose a spatial-conditioning variant that establishes a promising path towards generalization to unseen geometries. Ultimately, conditional drifting serves as a highly efficient alternative to diffusion based approaches, unlocking real-time CFD surrogates where inference speed is critical.