用漂移生成框架实现流体模拟的实时高精度替代。
Drifting Models for Surrogate Flow Modeling
- 在变分自编码器潜空间中进行条件漂移，支持单次生成。
- 精度与迭代扩散模型相当，推理速度提升100倍。
- 适用于快速优化室内环境，适合需要实时生成的场景。
虽然计算流体力学（CFD）能提供高保真流场以优化室内环境，但其计算成本限制了快速探索。生成式代理模型相较于确定性网络能更好建模分布，但迭代采样速度慢。为实现高质量、单次通过的生成，我们将新颖的生成漂移框架应用于流体力学。提出一种条件架构，在学习的变分自编码器（VAE）潜空间中执行漂移，并利用标签感知掩码对齐生成样本与边界条件。所提条件模型在精度和流场一致性上与迭代扩散方法相当，但推理速度提升两个数量级。此外，我们还提出一种空间条件变体，为推广至未见几何结构提供了可行路径。最终，条件漂移成为基于扩散模型方法的高效替代方案，使实时CFD代理模型成为可能，尤其适用于推理速度至关重要的场景。
原文摘要 · Abstract (English)
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.
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