The popularity of deep convolutional autoencoders (CAEs) has engendered effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems. However, it is not known whether deep CAEs provide superior performance in all ROM scenarios. To elucidate this, the effect of autoencoder architecture on its associated ROM is studied through the comparison of deep CAEs against two alternatives: a simple fully connected autoencoder, and a novel graph convolutional autoencoder. Through benchmark experiments, it is shown that the superior autoencoder architecture for a given ROM application is highly dependent on the size of the latent space and the structure of the snapshot data, with the proposed architecture demonstrating benefits on data with irregular connectivity when the latent space is sufficiently large.

翻译：深相电解解码器的普及为模拟大型动态系统创造了有效的减序模型(ROMs),但是,尚不清楚深相电解码器是否在所有ROM情景中都具有优异性能。为了澄清这一点,通过对深相电解码器结构对其相关ROM的影响进行比较,通过对深相电解码器与两种替代方案(一个简单完全连接的自动解码器,和一个新型的图形相电解码器)。通过基准实验,可以发现,某个特定ROM应用的高级自动解码器结构在很大程度上取决于潜在空间的大小和快照数据的结构,而拟议的结构则表明,在潜在空间足够大的情况下,数据连接性不规律会有好处。