# Variational Autoencoder based Metamodeling for Multi-Objective Topology Optimization of Electrical Machines

arXiv: https://arxiv.org/abs/2201.08877 · HTML: https://mlsanity.com/paper/2201.08877/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 21 Jan 2022.

## Builds on (most cited first, 6 of 6)

- [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)
- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [Variational Autoencoder for Deep Learning of Images, Labels and Captions](https://mlsanity.com/paper/1609.08976/) (2016)
- [Deep Learning-based Prediction of Key Performance Indicators for Electrical Machine](https://mlsanity.com/paper/2012.11299/) (2020)

## Built on by (most cited first, 5 of 5)

- [DADO -- Low-Cost Query Strategies for Deep Active Design Optimization](https://mlsanity.com/paper/2307.04536/) (2023)
- [Performance Analysis of Electrical Machines Using a Hybrid Data- and Physics-Driven Model](https://mlsanity.com/paper/2201.09603/) (2022)
- [Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation](https://mlsanity.com/paper/2309.13179/) (2023)
- [Deep learning based Meta-modeling for Multi-objective Technology Optimization of Electrical Machines](https://mlsanity.com/paper/2306.09087/) (2023)
- [A Multi-stage Constrained Optimization Framework for Data-driven Problems](https://mlsanity.com/paper/2607.23480/) (2026)

## Abstract (arXiv)

Conventional magneto-static finite element analysis of electrical machine design is time-consuming and computationally expensive. Since each machine topology has a distinct set of parameters, design optimization is commonly performed independently. This paper presents a novel method for predicting Key Performance Indicators (KPIs) of differently parameterized electrical machine topologies at the same time by mapping a high dimensional integrated design parameters in a lower dimensional latent space using a variational autoencoder. After training, via a latent space, the decoder and multi-layer neural network will function as meta-models for sampling new designs and predicting associated KPIs, respectively. This enables parameter-based concurrent multi-topology optimization.