A Fast Learning-Based Surrogate of Electrical Machines using a Reduced Basis
This work addresses the need for efficient surrogate models in engineering domains like electrical machines, though it is incremental as it combines existing techniques.
The paper tackled the problem of building fast surrogate models for parameterized PDEs in electrical machines by hybridizing Proper Orthogonal Decomposition and Support Vector Regression, achieving real-time performance for digital twin applications with promising results on industrial use cases.
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.