论文标题
CFD数据压缩的物理驱动的卷积自动编码器方法
Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions
论文作者
论文摘要
随着湍流模型的增长和复杂性的增长，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，已经提出了基于原位的自动编码器的压缩方法，并证明可以有效地产生湍流数据的减少表示形式。但是，这些方法仅着眼于使用未利用湍流物理特性的点样品重建损失训练模型。 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可以促进使用可解释性技术的映射，（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。
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.
