Modeling Finite-Element Constraint to Run an Electrical Machine Design Optimization Using Machine Learning

This paper proposes a method to the model constraints from different models to run an optimization over models with different granularities. Through machine learning, the proposed method has proven to be able to accurately map the constraints and minimize the number of call to the model. It handles...

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Published in:IEEE transactions on magnetics 2015-03, Vol.51 (3), p.1-4
Main Authors: Arnoux, Pierre-Hadrien, Caillard, Pierre, Gillon, Frederic
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Computational modeling
Design optimization
Entropy
Finite element method
Handles
Iron
Machine learning
Magnetism
Mathematical analysis
Mathematical models
Mixes
Optimization
Prediction algorithms
Statistics
Vegetation
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description This paper proposes a method to the model constraints from different models to run an optimization over models with different granularities. Through machine learning, the proposed method has proven to be able to accurately map the constraints and minimize the number of call to the model. It handles both continuous and discrete variables and mixes design rules to statistic approach to create a surrogate of the model.
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source IEEE Electronic Library (IEL) Journals
subjects Algorithm design and analysis
Computational modeling
Design optimization
Entropy
Finite element method
Handles
Iron
Machine learning
Magnetism
Mathematical analysis
Mathematical models
Mixes
Optimization
Prediction algorithms
Statistics
Vegetation
title Modeling Finite-Element Constraint to Run an Electrical Machine Design Optimization Using Machine Learning
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