Deep Learning and Reduced Models for Fast Optimization in Electromagnetics

Barmada, Sami; Fontana, Nunzia; Sani, Luca; Thomopulos, Dimitri; Tucci, Mauro

doi:10.1109/tmag.2019.2957197

Cited by 46 publications

(41 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, there is more research carried out on machine learning for improving the runtime of the optimization of electromagnetic devices, as was also shown in Table 2. Different machine learning algorithms, such as SVM, multi-layer perceptron (MLP), Knearest neighbor (KNN), and CNN have been investigated to optimize transformers, antennas, and motors (motors are the majority applications) [135][136][137][138][139][140][141][142][146][147][148][149][150]. It is noted that deep learning follows promising results when applied for topology optimization of electromagnetic devices, and this topic has attracted much attention recently [143][144][145].…”

Section: Machine Learning For Optimization Of Electromagnetic Devicesmentioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

show abstract

Section: Machine Learning For Optimization Of Electromagnetic Devicesmentioning

confidence: 99%

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Lei

Bramerdorfer

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…One of the recent work demonstrates a multi-layer perceptron as a meta-model for shape optimization of PMSMs [39]. In the recent past, a combination of the CNN based model and a reduced FE model were analyzed for accelerated optimizations in electromagnetics [40].…”

Section: Design Evaluation and Pareto Front Createdmentioning

confidence: 99%

Deep Learning-Based Prediction of Key Performance Indicators for Electrical Machines

2021

View full text Add to dashboard Cite

The design of an electrical machine can be quantified and evaluated by Key Performance Indicators (KPIs) such as maximum torque, critical field strength, costs of active parts, sound power, etc. Generally, cross-domain tool-chains are used to optimize all the KPIs from different domains (multiobjective optimization) by varying the given input parameters in the largest possible design space. This optimization process involves magneto-static finite element simulation to obtain these decisive KPIs. It makes the whole process a vehemently time-consuming computational task that counts on the availability of resources with the involvement of high computational cost. In this paper, a data-aided, deep learningbased meta-model is employed to predict the KPIs of an electrical machine quickly and with high accuracy to accelerate the full optimization process and reduce its computational costs. The focus is on analyzing various forms of input data that serve as a geometry representation of the machine. Namely, these are the cross-section image of the electrical machine that allows a very general description of the geometry relating to different topologies and the the classical way of scalar geometry parametrizations. The impact of the resolution of the image is studied in detail. The results show a high prediction accuracy and proof that deep learning-based meta-models are able to minimize the optimization time. The results also indicate that the prediction quality of an image-based approach can be made comparable to the classical way based on scalar parameters.

show abstract

“…Stator current and PM grade remain unchanged for all experiments. The same approach can be used to obtain reluctance motor rotor geometries for DL studies [22]. Three phase 60 Hz excitation is applied in all models.…”

Section: Fea Analysis Of Ipmsmsmentioning

confidence: 99%

“…Electrical machine researchers previously used very deep architectures such as GoogLeNet [13], [27], and even VGG 16 that has 138 m parameters [14], [28]. However, smaller custom architectures were also used successfully [22]. Thus, it is currently hard to determine the optimal CNN architecture required for electrical machine design projects.…”

Section: DL Cnn Architectures and Training Parametersmentioning

confidence: 99%

Towards End-to-end Deep Learning Analysis of Electrical Machines

Gabdullin¹,

Madanzadeh²,

Vilkin³

2020

Preprint

View full text Add to dashboard Cite

<p>Convolutional Neural Networks (CNNs) and Deep Learning (DL) revolutionized numerous research fields including robotics, natural language processing, self-driving cars, healthcare, and others. However, DL is still relatively under-researched in fields such as physics and engineering. Recent works on DL-assisted analysis showed emerging interest and enormous potential of CNN applications. This paper explores the possibility of developing an end-to-end DL pipeline for the analysis of electrical machines. The CNNs are trained on conventional finite element method (FEA) data to predict the output torque curves of electric machines. FEA is only used for dataset collections and CNN training, whereas the analysis is done solely using CNNs. The required depth in CNN architecture is studied by comparing a simplistic CNN with three ResNet architectures. The effects of dataset balancing and data normalization are studied and torque clipping inspired by offset normalization is proposed to ease CNN training and improve the prediction accuracy. The relation between architecture depth and accuracy is identified showing that deeper CNNs improve the curve shape prediction accuracy even after torque magnitude prediction accuracy saturates. Over 90% accuracy for analysis conducted under a minute is reported for CNNs, whereas FEA of comparable accuracy required 200 hours. Predicting multidimensional outputs can improve CNN performance, which is essential for multiparameter optimization of electrical machines. </p>

show abstract

Deep Learning and Reduced Models for Fast Optimization in Electromagnetics

Cited by 46 publications

References 6 publications

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

Deep Learning-Based Prediction of Key Performance Indicators for Electrical Machines

Towards End-to-end Deep Learning Analysis of Electrical Machines

Contact Info

Product

Resources

About