# Deep learning based Meta-modeling for Multi-objective Technology Optimization of Electrical Machines

arXiv: https://arxiv.org/abs/2306.09087 · HTML: https://mlsanity.com/paper/2306.09087/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine](https://mlsanity.com/paper/2012.11299/) (2020)
- [Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines](https://mlsanity.com/paper/2201.08877/) (2022)

## Abstract (arXiv)

Optimization of rotating electrical machines is both time- and computationally expensive. Because of the different parametrization, design optimization is commonly executed separately for each machine technology. In this paper, we present the application of a variational auto-encoder (VAE) to optimize two different machine technologies simultaneously, namely an asynchronous machine and a permanent magnet synchronous machine. After training, we employ a deep neural network and a decoder as meta-models to predict global key performance indicators (KPIs) and generate associated new designs, respectively, through unified latent space in the optimization loop. Numerical results demonstrate concurrent parametric multi-objective technology optimization in the high-dimensional design space. The VAE-based approach is quantitatively compared to a classical deep learning-based direct approach for KPIs prediction.