在后处理阶段利用扩散模型减少基于深度学习的城市微气候预测的时空误差
Using Diffusion Models for Reducing Spatiotemporal Errors of Deep Learning Based Urban Microclimate Predictions at Post-Processing Stage
AI总结：
本文提出将DDPM作为CAE与U-Net城市微气候预测结果的后处理方法，以抑制序列时空误差累积，最高提升准确率65%并保持3倍加速。
摘要：
计算流体力学（CFD）是模拟湍流的强大工具，通常用于城市微气候模拟。然而，传统CFD方法计算密集，高保真模拟需要大量硬件资源。深度学习（DL）模型正作为高效替代方案而受到欢迎，因为它们只需较少计算资源即可模拟流体流动中的复杂非线性相互作用。DL模型的一个主要缺点是在长期时间预测中容易出现误差累积，往往损害其准确性和可靠性。为解决这一不足，本研究考察使用去噪扩散概率模型（DDPM）作为一种新型后处理技术，以缓解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架构来预测立方体结构周围的气流动力学。随后将DDPM应用于这些模型的预测，细化重建流场，使其更符合从大涡模拟获得的高保真统计结果。结果表明，尽管深度学习模型相较传统数值求解器具有显著计算优势，但它们在序列预测中易受误差累积影响；然而，将DDPM用作后处理步骤可将DL模型的准确性提高至多65%，同时相较传统数值求解器保持3倍加速。这些发现凸显了整合去噪扩散概率模型作为一种变革性方法的潜力，可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、可扩展的流体动力学建模铺平道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
