Variational autoencoder-based techniques for a streamlined cross-topology modeling and optimization workflow in electrical drives  

作　　者：Marius Benkert Michael Heroth Rainer Herrler Magda Gregorová Helmut C.Schmid 

机构地区：[1]CAIRO,THWS,Franz-Horn-Str 2,Würzburg,97082,Germany [2]Advanced Development E-Motor Simulation,ZF Friedrichshafen AG,Rotgenstr.2,97424 Schweinfurt,Germany

出　　处：《Autonomous Intelligent Systems》2024年第1期297-306,共10页自主智能系统(英文)

摘　　要：The generation and optimization of simulation data for electrical machines remain challenging,largely due to the complexities of magneto-staticfinite element analysis.Traditional methodologies are not only resource-intensive,but also time-consuming.Deep learning models can be used to shortcut these calculations.However,challenges arise when considering the unique parameter sets specific to each machine topology.Building on two recent studies(Parekh et al.in IEEE Trans.Magn.58(9):1-4,2022;Parekh et al.,Deep learning based meta-modeling for multi-objective technology optimization of electrical machines,2023,arXiv:2306.09087),that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization,this paper proposes a refined architecture and optimization workflow.Our modifications aim to streamline and enhance the robustness of both the training and optimization processes,and compare the results with the variational autoencoder architecture proposed recently.

关 键 词：Deep learning Design optimization Electrical machines Variational autoencoder 

分 类 号：O17[理学—数学]