用扩散模型优化城市湍流监测传感器布局并高保真重建风场
Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments
- 结合扩散模型与贝叶斯推断，实现高效传感器最优部署
- 在极稀疏采样下仍保持统计与瞬时流场精度
- 可零样本部署，适合急需快速响应的城市环境监测
快速城市化要求对湍流风场进行精准高效监测，以支持空气质量、气候韧性及基础设施设计。传统稀疏重构与传感器布置方法在实际约束下精度显著下降。本文提出 Diff-SPORT，一种基于扩散模型的高保真流场重建与城市环境传感器最优布置框架。该方法融合生成式扩散模型、最大后验（MAP）推断与谢尔利值归因分析，提供可扩展且可解释的解决方案。相比传统数值方法，Diff-SPORT 在保持统计与瞬时流场保真度的同时实现显著加速。本方法具备模块化、零样本特性，无需重新训练即可应对极端稀疏采样，为城市流场实时可靠监测提供新路径。该工作推动了生成建模与可解释性在可持续城市智能中的集成应用。
原文摘要 · Abstract (English)
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.
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