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Di Barba

As seen in: arXiv, MDPI
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Articles

Synthesis of Boundary Conditions in Polygonal Magnetic Domains Using Deep Neural Networks

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By Sami Barmada, Paolo Di Barba, Maria Evelina Mognaschi, Di Barba
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Energy-based PINNs for solving coupled field problems: concepts and application to the optimal design of an induction heater

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By Di Barba, Paolo Di Barba
| arXiv
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Electromagnetic Wave Absorption in the Human Head: A Virtual Sensor Based on a Deep-Learning Model

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By Paolo Di Barba, Maria Evelina Mognaschi, Di Barba, Jarosław Kawecki
| MDPI
3. Results For the CNN training, a database of 1387 samples is used, and the 80/20 rule, i.e., 80% of the dataset for the training set and 20% for the validation set, is applied. The ADAM solver, i.e., an optimization algorithm based on the stochastic gradient descent, is used, and 800 epochs, a batch size of 32, and an initial learning rate of 10−6 are considered for the training.
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