Learning electromagnetic fields based on finite element basis functions

TL;DR

This paper introduces a novel method combining isogeometric analysis, POD, and deep learning to efficiently predict electromagnetic fields in electric machines with geometry variations, ensuring physical consistency.

Contribution

It presents a new approach that directly learns spline basis coefficients for rapid, accurate, and physically consistent electromagnetic field predictions in electric machine models.

Findings

Effective prediction of electromagnetic fields in a nonlinear magnetostatic model.

Demonstrated rapid and physically consistent results with the proposed method.

Applicable to parametric models of electric machines with geometry variations.

Abstract

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.