A surrogate model approximates the outputs of a solver of Partial Differential Equations (PDEs) with a low computational cost. In this article, we propose a method to build learning-based surrogates in the context of parameterized PDEs, which are PDEs that depend on a set of parameters but are also temporal and spatial processes. Our contribution is a method hybridizing the Proper Orthogonal Decomposition and several Support Vector Regression machines. This method is conceived to work in real-time, thus aimed for being used in the context of digital twins, where a user can perform an interactive analysis of results based on the proposed surrogate. We present promising results on two use cases concerning electrical machines. These use cases are not toy examples but are produced an industrial computational code, they use meshes representing non-trivial geometries and contain non-linearities.

本文介绍了一种基于学习的代理模型构建方法，该方法将Proper Orthogonal Decomposition和多个Support Vector Regression机器相结合，用于实时参数化偏微分方程(PDEs)的求解，以实现数字孪生中的交互式分析。该方法在两个关于电机的实际应用案例中展现了良好的结果。

一种基于快速学习的电机简化模型