Parametric surrogate models of electric machines are widely used for efficient design optimization and operational monitoring. Addressing geometry variations, spline-based computer-aided design representations play a pivotal role. In this study, we propose a novel approach that combines isogeometric analysis, proper orthogonal decomposition and deep learning to enable rapid and physically consistent predictions by directly learning spline basis coefficients. The effectiveness of this method is demonstrated using a parametric nonlinear magnetostatic model of a permanent magnet synchronous machine.

翻译：电机参数化代理模型被广泛应用于高效的设计优化与运行监测。针对几何形状变化问题，基于样条的计算机辅助设计表示方法发挥着关键作用。本研究提出一种创新方法，将等几何分析、本征正交分解与深度学习相结合，通过直接学习样条基函数系数实现快速且物理一致的预测。该方法通过永磁同步电机的参数化非线性静磁模型验证了其有效性。