Surrogate Modeling of Electrical Machine Torque Using Artificial Neural Networks

Mikko Tahkola (Corresponding Author), Janne Keränen, Denis Sedov, Mehrnaz Farzam Far, Juha Kortelainen

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)
275 Downloads (Pure)


Machine learning and artificial neural networks have shown to be applicable in modeling and simulation of complex physical phenomena as well as creating surrogate models trained with physics-based simulation data for numerous applications that require good computational performance. In this article, we review widely the surrogate modeling concept and its applications in the electrical machine context. We present comprehensively a workflow for developing data-driven surrogate models including data generation with physics-based simulation and design of experiments, preprocessing of training data, and training and testing of the surrogates. We compare neural networks and gradient boosting decision trees in modeling and simulation of torque behavior of a permanent magnet synchronous machine together with selected design of experiments approaches with respect to surrogate accuracy and computational efficiency. In addition, an approach to utilizing domain knowledge to create a hybrid surrogate model in order to improve the surrogate accuracy is shown. The accuracy of the selected hybrid neural network was better than with the gradient boosting approach and was close to the finite element simulation, whereas its run-time efficiency was significantly better compared to the finite element simulation with a speed-up factor of over 2,000. In addition, combining the sampling methods provided better results than the selected methods alone.
Original languageEnglish
Pages (from-to)220027-220045
JournalIEEE Access
Publication statusPublished - 7 Dec 2020
MoE publication typeA1 Journal article-refereed


This work was supported by the Arrowhead Tools Project, funded by the European Commission through the European H2020 Research and Innovation Programme, ECSEL Joint Undertaking, and National Funding Authorities from the 18 involved countries under Grant 826452.


  • Artificial neural networks
  • design of experiments
  • electromagnetic modeling
  • machine learning
  • numerical simulation
  • permanent magnet machine
  • surrogate model


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