高速な深層学習メッシュフリー数値解析のためのニューラルモード分解の定式化と自由表面流れの再構成

抄録

Reduced Order Modeling (ROM) reduces the computational cost in simulating physics phenomena by using reduced dimensional spaces. However, it becomes difficult to apply ROM to reconstruction of physical fields represented by the Lagrangian mechanics, such as the particle method, in the numerical analysis of free surface flows. This study aims to create a ROM applicable to free surface flows of Lagrangian description. A novel deep learning-based mode decomposition method, which can be applied to simulate physics phenomena obtained by the Lagrange method, is proposed as a component of ROM in this paper. Validation of proposed method was carried out for the analysis of water drop problem. The results showed that the original physical field can be reconstructed with high accuracies from the modes obtained by NMD realized deep learning.