# Multi-Objective Optimization of Electrical Machines using a Hybrid Data-and Physics-Driven Approach

arXiv: https://arxiv.org/abs/2306.09096 · HTML: https://mlsanity.com/paper/2306.09096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 8 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 2 of 2)

- [Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine](https://mlsanity.com/paper/2012.11299/) (2020)
- [Performance Analysis of Electrical Machines Using a Hybrid Data- and Physics-Driven Model](https://mlsanity.com/paper/2201.09603/) (2022)

## Abstract (arXiv)

Magneto-static finite element (FE) simulations make numerical optimization of electrical machines very time-consuming and computationally intensive during the design stage. In this paper, we present the application of a hybrid data-and physics-driven model for numerical optimization of permanent magnet synchronous machines (PMSM). Following the data-driven supervised training, deep neural network (DNN) will act as a meta-model to characterize the electromagnetic behavior of PMSM by predicting intermediate FE measures. These intermediate measures are then post-processed with various physical models to compute the required key performance indicators (KPIs), e.g., torque, shaft power, and material costs. We perform multi-objective optimization with both classical FE and a hybrid approach using a nature-inspired evolutionary algorithm. We show quantitatively that the hybrid approach maintains the quality of Pareto results better or close to conventional FE simulation-based optimization while being computationally very cheap.