标题： 基于扩散的都市环境湍流传感器布局优化与流场重建：Diff-SPORT 显示英文标题

标题： Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments

摘要： 快速城市化要求对湍流风模式进行准确且高效的监测，以支持空气质量、气候韧性和基础设施设计。 传统稀疏重建和传感器布局策略在实际约束下面临主要的准确性下降。 在这里，我们引入了 Diff-SPORT，这是一种基于扩散的框架，用于城市环境中的高保真流动重建和最佳传感器布局。 Diff-SPORT 结合了生成扩散模型、最大后验（MAP）推理方案以及 Shapley 值归因框架，提出了一种可扩展且可解释的解决方案。 与传统数值方法相比，Diff-SPORT 在保持统计和瞬时流动保真度的同时实现了显著的速度提升。 我们的方法提供了一种模块化的、无需微调的替代方案，支持在极端稀疏条件下进行快速且可靠的都市流动监测。 Diff-SPORT 为在可持续城市智能中整合生成建模和可解释性铺平了道路。 显示英文摘要

摘要： Rapid urbanization demands accurate and efficient monitoring of turbulent wind patterns to support air quality, climate resilience and infrastructure design. Traditional sparse reconstruction and sensor placement strategies face major accuracy degradations under practical constraints. Here, we introduce Diff-SPORT, a diffusion-based framework for high-fidelity flow reconstruction and optimal sensor placement in urban environments. Diff-SPORT combines a generative diffusion model with a maximum a posteriori (MAP) inference scheme and a Shapley-value attribution framework to propose a scalable and interpretable solution. Compared to traditional numerical methods, Diff-SPORT achieves significant speedups while maintaining both statistical and instantaneous flow fidelity. Our approach offers a modular, zero-shot alternative to retraining-intensive strategies, supporting fast and reliable urban flow monitoring under extreme sparsity. Diff-SPORT paves the way for integrating generative modeling and explainability in sustainable urban intelligence.