基于有限元统计数据驱动的中频变压器设计优化建模方法

This article proposes a novel class of neural-network-inspired statistical data-driven models, especially derived for the purpose of design optimization of medium-frequency transformers. These models allow for an efficient [three to four orders of magnitude faster compared to a finite-element method (FEM)] yet sufficiently accurate (within 5-10% error relative to the FEM) and numerically stable es...