Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Curti, Mitrofan"'
This work presents a simple and robust method to construct a B-spline based Everett map, for application in the Preisach model of hysteresis, to predict static hysteresis behavior. Its strength comes from the ability to directly capture the Everett m
Externí odkaz:
http://arxiv.org/abs/2410.02797
Publikováno v:
IEEE Transactions on Magnetics 2024
Hysteresis modeling is crucial to comprehend the behavior of magnetic devices, facilitating optimal designs. Hitherto, deep learning-based methods employed to model hysteresis, face challenges in generalizing to novel input magnetic fields. This pape
Externí odkaz:
http://arxiv.org/abs/2407.03261
Autor:
Chandra, Abhishek, Kapoor, Taniya, Daniels, Bram, Curti, Mitrofan, Tiels, Koen, Tartakovsky, Daniel M., Lomonova, Elena A.
Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We develop an ordinary differential equation-based recurrent neural networ
Externí odkaz:
http://arxiv.org/abs/2308.12002
Autor:
Chandra, Abhishek, Daniels, Bram, Curti, Mitrofan, Tiels, Koen, Lomonova, Elena A., Tartakovsky, Daniel M.
This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques. While sparse regression has previously been used to model var
Externí odkaz:
http://arxiv.org/abs/2302.05313
Autor:
Pourkeivannour, Siamak, van Zwieten, Joost S. B., Friedrich, Léo A. J., Curti, Mitrofan, Lomonova, Elena A.
Publikováno v:
J: Multidisciplinary Scientific Journal; Dec2023, Vol. 6 Issue 4, p627-638, 12p
Akademický článek
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Publikováno v:
24th International Conference on the Computation of Electromagnetic Fields, COMPUMAG 2023
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=narcis______::e245f671790a3434dc954da65af253d3
https://research.tue.nl/nl/publications/5a23654f-163e-47d4-85a0-8c369d47897f
https://research.tue.nl/nl/publications/5a23654f-163e-47d4-85a0-8c369d47897f
Autor:
Chandra, Abhishek, Curti, Mitrofan, Tiels, Koen, Lomonova, Elena A., Tartakovsky, Daniel M., Ishibuchi, Hisao, Kwoh, Chee-Keong, Tan, Ah-Hwee, Srinivasan, Dipti, Miao, Chunyan, Trivedi, Anupam, Crockett, Keeley
Publikováno v:
2022 IEEE Symposium Series on Computational Intelligence (SSCI), 1451-1459
STARTPAGE=1451;ENDPAGE=1459;TITLE=2022 IEEE Symposium Series on Computational Intelligence (SSCI)
STARTPAGE=1451;ENDPAGE=1459;TITLE=2022 IEEE Symposium Series on Computational Intelligence (SSCI)
This paper investigates the application of Physics-Informed Neural Networks (PINNs) in modelling constitutive laws for transverse electromagnetic polarized waves in all three space dimensions governed by Maxwell Faraday equation and Ampere's circuita
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9806b643998bd20790f9a63c9c15cb10
https://doi.org/10.1109/ssci51031.2022.10022292
https://doi.org/10.1109/ssci51031.2022.10022292
Publikováno v:
CEFC 2022-20th Biennial IEEE Conference on Electromagnetic Field Computation, Proceedings
The Everett map is a component used by the Preisach model that stores the hysteresis behavior of soft-magnetic materials, and allows for accurate modeling of hysteresis loops when properly constructed. It is created from scattered data points, obtain
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92171ed5fec57d680948ff51bbc124b5
https://doi.org/10.1109/cefc55061.2022.9940632
https://doi.org/10.1109/cefc55061.2022.9940632
Akademický článek
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