tLOSS: a collaborative machine learning platform for predicting AC losses in HTS devices - Archive ouverte HAL
Communication Dans Un Congrès Année : 2022

tLOSS: a collaborative machine learning platform for predicting AC losses in HTS devices

Résumé

This work describes an open access platform for data-driven modelling of AC losses in high-temperature superconducting devices, as opposed to computationally intensive, time-consuming numerical methods. The platform is being developed in the frame of the Portuguese project tLOSS.
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Dates et versions

hal-03791605 , version 1 (15-12-2022)

Identifiants

  • HAL Id : hal-03791605 , version 1

Citer

Miguel Vieira, Joao Rosas, Joao Murta-Pina, Roberto Oliveira, Henrique Simas, et al.. tLOSS: a collaborative machine learning platform for predicting AC losses in HTS devices. 8th International Workshop on Numerical Modelling of High Temperature Superconductors (HTS 2022), Kévin Berger (Université de Lorraine - GREEN), Jun 2022, Nancy, France. ⟨hal-03791605⟩

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