Simulation techniques such as the finite element method are essential for designing electrical devices, but their computational cost can be prohibitive for repeated or real-time computations. Projection-based model order reduction techniques mitigate this by reducing the model size and complexity, yet face challenges when extended to nonlinear or non-affine parametric models. In this work, Isogeometric Analysis (IGA) is combined with proper orthogonal decomposition and Gaussian process regression to construct a non-intrusive surrogate model of a parametric nonlinear model of a permanent magnet synchronous machine. The differentiable nature of IGA allows for computationally efficient extraction of parametric sensitivities, which are leveraged for gradient-enhanced surrogate modeling.

翻译：有限元法等仿真技术对于电气设备设计至关重要，但其计算成本在重复或实时计算中可能过高。基于投影的模型降阶技术通过减小模型规模和复杂度来缓解这一问题，但在扩展到非线性或非仿射参数模型时面临挑战。本研究将等几何分析（IGA）与适当正交分解及高斯过程回归相结合，构建了永磁同步电机参数化非线性模型的非侵入式代理模型。IGA的可微特性允许以计算高效的方式提取参数灵敏度，这些灵敏度被用于梯度增强的代理建模。