用预训练视频模型快速模拟城市风场，支持直接优化建筑布局。
Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
- 将视频扩散模型转为可微分风场模拟器，替代耗时的流体计算。
- 1秒内生成112帧风场动态，性能超越专用神经微分方程求解器。
- 支持端到端反向传播，自动优化建筑位置提升行人舒适度与安全。
设计满足行人风舒适性与安全性的城市空间需依赖高时间分辨率的计算流体力学（CFD）模拟，但当前计算成本过高，难以开展广泛设计探索。本文提出WinDiNet（风扩散网络），一个基于预训练视频扩散模型的快速可微分代理模型。以20亿参数的LTX-Video为基础，在10,000组程序化生成的建筑布局上进行微调，涵盖二维不可压缩流体仿真。通过系统研究训练策略、条件机制及VAE适配方法，包括引入物理信息解码损失，获得优于专用神经偏微分方程求解器的配置。模型可在1秒内生成完整的112帧动态风场序列。由于其端到端可微特性，可作为梯度驱动的逆向优化物理引擎：给定城市轮廓布局，通过反向传播直接优化建筑位置，显著提升风安全与行人舒适度。在单入口与多入口布局上的实验表明，优化器能发现有效布局，所有改进均经真实CFD验证。
原文摘要 · Abstract (English)
Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but their current computational cost makes extensive design exploration impractical. We introduce WinDiNet (Wind Diffusion Network), a pretrained video diffusion model that is repurposed as a fast, differentiable surrogate for this task. Starting from LTX-Video, a 2B-parameter latent video transformer, we fine-tune on 10,000 2D incompressible CFD simulations over procedurally generated building layouts. A systematic study of training regimes, conditioning mechanisms, and VAE adaptation strategies, including a physics-informed decoder loss, identifies a configuration that outperforms purpose-built neural PDE solvers. The resulting model generates full 112-frame rollouts in under a second. As the surrogate is end-to-end differentiable, it doubles as a physics simulator for gradient-based inverse optimization: given an urban footprint layout, we optimize building positions directly through backpropagation to improve wind safety as well as pedestrian wind comfort. Experiments on single- and multi-inlet layouts show that the optimizer discovers effective layouts even under challenging multi-objective configurations, with all improvements confirmed by ground-truth CFD simulations.
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