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Purnode Florent

Dép. d'électric., électron. et informat. (Inst.Montefiore) > Applied and Computational Electromagnetics (ACE)

Montefiore Institute

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Main Referenced Co-authors
Geuzaine, Christophe  (9)
Henrotte, François  (9)
Louppe, Gilles  (8)
Caire François (1)
Caire, Francois (1)
Main Referenced Keywords
Magnetic hysteresis (7); Magnetic losses (7); Nonhomogeneous media (6); Neural networks (4); Lamination (3);
Main Referenced Disciplines
Electrical & electronics engineering (9)
Materials science & engineering (8)

Publications (total 9)

The most downloaded
114 downloads
Purnode, F., Henrotte, F., Caire, F., Da Silva, J., Louppe, G., & Geuzaine, C. (2022). A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime. IEEE Transactions on Magnetics. doi:10.1109/TMAG.2022.3160651 https://hdl.handle.net/2268/291443

The most cited

2 citations (Scopus®)

Purnode, F., Henrotte, F., Caire, F., Da Silva, J., Louppe, G., & Geuzaine, C. (2022). A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime. IEEE Transactions on Magnetics. doi:10.1109/TMAG.2022.3160651 https://hdl.handle.net/2268/291443

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (02 April 2024). Neural network-based simulation of fields and losses in electrical machines with ferromagnetic laminated cores. International Journal of Numerical Modelling, 37 (2). doi:10.1002/jnm.3226
Peer Reviewed verified by ORBi

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (04 September 2023). Fast and accurate Neural-Network-based Ferromagnetic Laminated Stack Model for Electrical Machine Simulations in Periodic Regime [Paper presentation]. COMPUMAG 2023, Kyoto, Japan.
Peer reviewed

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (30 August 2023). Neural-Network-Based Identification of Material Law Parameters for Fast and Accurate Simulations of Electrical Machines in Periodic Regime [Poster presentation]. EMF 2023, Marseille, France.

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (12 July 2023). Neural-Network-Based Identification of Material Law Parameters for Fast and Accurate Simulations of Electrical Machines in Periodic Regime [Paper presentation]. EMF 2023, Marseille, France.
Peer reviewed

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (26 May 2023). Fast and accurate Neural-Network-based Ferromagnetic Laminated Stack Model for Electrical Machine Simulations in Periodic Regime [Paper presentation]. COMPUMAG 2023, Kyoto, Japan.

Purnode, F., Henrotte, F., Louppe, G., & Geuzaine, C. (01 September 2022). A Homogenized Material Law based on Neural Networks for the Accurate Prediction of Losses in Electrical Machines [Paper presentation]. ACOMEN 2022, Liège, Belgium.

Purnode, F., Henrotte, F., Caire, F., Da Silva, J., Louppe, G., & Geuzaine, C. (2022). A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime. IEEE Transactions on Magnetics. doi:10.1109/TMAG.2022.3160651
Peer Reviewed verified by ORBi

Purnode, F., Henrotte, F., Caire François, Da Silva Joaquim, Louppe, G., & Geuzaine, C. (18 January 2022). A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime [Poster presentation]. COMPUMAG 2021.

Purnode, F., Henrotte, F., & Geuzaine, C. (31 October 2021). A Material Law Based on Neural Networks and Homogenization for the Accurate Finite Element Simulation of Laminated Ferromagnetic Cores in the Periodic Regime [Paper presentation]. COMPUMAG 2021, Kyoto, Japan.
Peer reviewed

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