In this paper CNNs are used for solving an optimization problem with two different approaches: CNN is used as a surrogate model of the forward problem, inserted in an optimization loop governed by a genetic algorithm, in the first approach, while a CNN is trained for solving directly the inverse problem in the second approach. The case study is the shape design of a magnetic core used for material testing.

Convolutional neural networks for the shape design of a magnetic core for material testing: Forward and inverse approaches

Di Barba P.;Mognaschi M. E.
;
Sieni E.;
2022-01-01

Abstract

In this paper CNNs are used for solving an optimization problem with two different approaches: CNN is used as a surrogate model of the forward problem, inserted in an optimization loop governed by a genetic algorithm, in the first approach, while a CNN is trained for solving directly the inverse problem in the second approach. The case study is the shape design of a magnetic core used for material testing.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1466295
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