Work

A Physics-Informed Gaussian Process Regression-Based Meta Model for Rapid Characterization of Permanent Magnet Synchronous Machines

Year: 2025

Type: conference-paper

Abstract: Optimizing an Interior Permanent Magnet Synchronous Machine (IPM) requires evaluating multiple working points for each candidate design. In case the design domain has many dimensions, multiple working points evaluations would require an impractical number of finite element method (FEM)-based simulations. This study proposes a novel strategy to build a meta-model to reduce the number of FEM-based simulations for a given optimization process. The study proposes a novel, physics-informed meta-model based on Gaussian Process Regression (GPR) aiming for rapid characterization of any given machine design. The meta-model uses an adapted version of Posterior Standard Deviation (PSD) to allow for an exact and detailed adaptive sampling strategy. The results show that the proposed meta-model presents a data-efficient approach capable of computing performance parameters with low error. Additionally, the characterization from the proposed meta-model agrees with the experimental data.

DOI: 10.1109/iemdc60492.2025.11061045

Authors: Marcelo D. Silva, Oluwaseun A. Badewa, Rosemary E. Alden, Pedram Asef, Dan M. Ionel, Sandra Eriksson

Institutions: Uppsala University, University of Kentucky, University College London

Language: English

Topic: Electric Motor Design and Analysis

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funder: National Science Foundation

Award: Graduate Research Fellowship Program (GRFP)