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.

翻译：尽管计算流体动力学（CFD）能为优化室内环境提供高保真流场，但其计算成本限制了快速探索。为解决此问题，生成式代理模型相比确定性网络能提供更好的分布建模，但迭代采样速度较慢。为实现高质量的单次生成，我们将新颖的生成式漂移框架适配至流体力学领域。我们引入一种条件架构，该架构在学得的VAE隐空间中进行漂移，并采用标签感知掩码使生成样本与边界条件对齐。我们的标签条件模型在准确性和流一致性上匹配迭代扩散方法，而运行速度则快两个数量级。此外，我们提出一种空间条件变体，为向未见几何体泛化开辟了有前景的路径。最终，条件漂移作为基于扩散方法的高效替代方案，在推理速度至关重要的场景中释放了实时CFD代理模型的潜力。