Deep neural networks for the efficient simulation of macro-scale hysteresis processes with generic excitation waveforms-Reference-Cited by-同舟云学术

Deep neural networks for the efficient simulation of macro-scale hysteresis processes with generic excitation waveforms

Published:2023-05 Issue: Volume:121 Page:105940
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Quondam-Antonio Simone^ORCID,Riganti-Fulginei Francesco^ORCID,Laudani Antonino^ORCID,Lozito Gabriele-Maria,Scorretti Riccardo

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

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4. An effective neural network approach to reproduce magnetic hysteresis in electrical steel under arbitrary excitation waveforms;Antonio;J. Magn. Magn. Mater.,2021

5. Numerical simulations of vector hysteresis processes via the Preisach model and the Energy Based Model: An application to Fe-Si laminated alloys;Antonio;J. Magn. Magn. Mater.,2021

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