CITATION.cff

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cff-version: 1.2.0
title: >-
  Hardcore: H-field and power loss estimation for arbitrary
  waveforms with residual, dilated convolutional neural
  networks in ferrite cores
message: >-
  MagNet Challenge 2023 Winning Solution (1st Place Model
  Performance)
type: software
authors:
  - given-names: Wilhelm
    family-names: Kirchgässner
    orcid: 'https://orcid.org/0000-0001-9490-1843'
    affiliation: Paderborn University
  - given-names: Nikolas
    family-names: Förster
    affiliation: Paderborn University
  - given-names: Till
    family-names: Piepenbrock
    affiliation: Paderborn University
  - given-names: Oliver
    family-names: Schweins
    affiliation: Paderborn University
  - given-names: Oliver
    family-names: Wallscheid
    affiliation: Siegen University
    orcid: 'https://orcid.org/0000-0001-9362-8777'
identifiers:
  - type: url
    value: 'https://arxiv.org/abs/2401.11488'
    description: arXiv 2401.11488
repository-code: 'https://github.com/upb-lea/hardcore-magnet-challenge'
abstract: >-
  Winning solution to the IEEE PELS MagNet challenge 2023
  (1st Place Model Performance Category)
keywords:
  - magnetics
  - neural networks
  - machine learning
  - artificial intelligence
  - transformer
  - power electronics
license: MIT