标题： 使用扩散模型在后处理阶段减少基于深度学习的城市微气候预测的时空误差 显示英文标题

标题： Using Diffusion Models for Reducing Spatiotemporal Errors of Deep Learning Based Urban Microclimate Predictions at Post-Processing Stage

摘要： 计算流体动力学（CFD）是一种用于建模湍流的强大工具，常用于城市微气候模拟。 然而，传统的CFD方法计算量大，需要大量的硬件资源来进行高保真模拟。 深度学习（DL）模型作为高效的替代方法越来越受欢迎，因为它们在建模流体流动中的复杂非线性相互作用时所需的计算资源较少。 DL模型的一个主要缺点是它们在长期时间预测中容易出现误差累积，这常常会损害其准确性和可靠性。 为解决这一不足，本研究探讨了去噪扩散概率模型（DDPM）作为一种新的后处理技术，以减轻DL模型序列预测中的误差传播。 为此，我们采用卷积自编码器（CAE）和U-Net架构来预测立方结构周围的气流动力学。 然后将DDPM应用于模型的预测结果，以更好地与从大涡模拟中获得的高保真统计结果对齐。 结果表明，尽管深度学习模型相比传统数值求解器具有显著的计算优势，但它们在序列预测中容易出现误差累积；然而，将DDPM作为后处理步骤可使DL模型的准确性提高高达65%，同时相比传统数值求解器保持3倍的速度提升。 这些发现突显了将去噪扩散概率模型整合为一种变革性方法的潜力，以提高基于深度学习的城市微气候模拟的可靠性和准确性，为更高效和可扩展的流体动力学建模铺平道路。 显示英文摘要

摘要： Computational fluid dynamics (CFD) is a powerful tool for modeling turbulent flow and is commonly used for urban microclimate simulations. However, traditional CFD methods are computationally intensive, requiring substantial hardware resources for high-fidelity simulations. Deep learning (DL) models are becoming popular as efficient alternatives as they require less computational resources to model complex non-linear interactions in fluid flow simulations. A major drawback of DL models is that they are prone to error accumulation in long-term temporal predictions, often compromising their accuracy and reliability. To address this shortcoming, this study investigates the use of a denoising diffusion probabilistic model (DDPM) as a novel post-processing technique to mitigate error propagation in DL models' sequential predictions. To address this, we employ convolutional autoencoder (CAE) and U-Net architectures to predict airflow dynamics around a cubic structure. The DDPM is then applied to the models' predictions, refining the reconstructed flow fields to better align with high-fidelity statistical results obtained from large-eddy simulations. Results demonstrate that, although deep learning models provide significant computational advantages over traditional numerical solvers, they are susceptible to error accumulation in sequential predictions; however, utilizing DDPM as a post-processing step enhances the accuracy of DL models by up to 65% while maintaining a 3 times speedup compared to traditional numerical solvers. These findings highlight the potential of integrating denoising diffusion probabilistic models as a transformative approach to improving the reliability and accuracy of deep learning-based urban microclimate simulations, paving the way for more efficient and scalable fluid dynamics modeling.