物理驱动的CFD数据通信卷积自编码方法
Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions
摘要：
随着湍流模型的规模和复杂性不断增长，数据压缩方法对于分析、可视化或重新启动模拟至关重要。最近，原位自动编码器为基础的压缩方法已被提出，并被证明是有效的生产减少表示的湍流数据。然而，这些方法仅仅集中于使用逐点样本重构损失来训练模型，而不利用湍流的物理性质。在本文中，我们证明了使用额外的物理信息正则化训练自动编码器，例如，增强不可压缩性和保持拟能，在三个方面改进了压缩模型：（i）压缩数据更好地符合均匀各向同性湍流的已知物理学，而不会对逐点重建质量产生负面影响，（ii）对训练模型的梯度的检查揭示了对学习的压缩映射的改变，这可以促进可解释性技术的使用，以及（iii）作为性能副产品，训练损失显示出比基线模型快12倍的收敛速度。
英文摘要：
With the growing size and complexity of turbulent flow models, data compression approaches are of the utmost importance to analyze, visualize, or restart the simulations. Recently, in-situ autoencoder-based compression approaches have been proposed and shown to be effective at producing reduced representations of turbulent flow data. However, these approaches focus solely on training the model using point-wise sample reconstruction losses that do not take advantage of the physical properties of turbulent flows. In this paper, we show that training autoencoders with additional physics-informed regularizations, e.g., enforcing incompressibility and preserving enstrophy, improves the compression model in three ways: (i) the compressed data better conform to known physics for homogeneous isotropic turbulence without negatively impacting point-wise reconstruction quality, (ii) inspection of the gradients of the trained model uncovers changes to the learned compression mapping that can facilitate the use of explainability techniques, and (iii) as a performance byproduct, training losses are shown to converge up to 12x faster than the baseline model.
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。
