---
_id: '63498'
author:
- first_name: Wilhelm
  full_name: Kirchgässner, Wilhelm
  last_name: Kirchgässner
- first_name: Nikolas
  full_name: Förster, Nikolas
  last_name: Förster
- first_name: Till
  full_name: Piepenbrock, Till
  last_name: Piepenbrock
- first_name: Oliver
  full_name: Schweins, Oliver
  last_name: Schweins
- first_name: Oliver
  full_name: Wallscheid, Oliver
  last_name: Wallscheid
citation:
  ama: 'Kirchgässner W, Förster N, Piepenbrock T, Schweins O, Wallscheid O. HARDCORE:
    H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated
    Convolutional Neural Networks in Ferrite Cores. <i>IEEE Transactions on Power
    Electronics</i>. 2025;40(2):3326-3335. doi:<a href="https://doi.org/10.1109/TPEL.2024.3488174">10.1109/TPEL.2024.3488174</a>'
  apa: 'Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., &#38; Wallscheid,
    O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms
    With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. <i>IEEE
    Transactions on Power Electronics</i>, <i>40</i>(2), 3326–3335. <a href="https://doi.org/10.1109/TPEL.2024.3488174">https://doi.org/10.1109/TPEL.2024.3488174</a>'
  bibtex: '@article{Kirchgässner_Förster_Piepenbrock_Schweins_Wallscheid_2025, title={HARDCORE:
    H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual, Dilated
    Convolutional Neural Networks in Ferrite Cores}, volume={40}, DOI={<a href="https://doi.org/10.1109/TPEL.2024.3488174">10.1109/TPEL.2024.3488174</a>},
    number={2}, journal={IEEE Transactions on Power Electronics}, author={Kirchgässner,
    Wilhelm and Förster, Nikolas and Piepenbrock, Till and Schweins, Oliver and Wallscheid,
    Oliver}, year={2025}, pages={3326–3335} }'
  chicago: 'Kirchgässner, Wilhelm, Nikolas Förster, Till Piepenbrock, Oliver Schweins,
    and Oliver Wallscheid. “HARDCORE: H-Field and Power Loss Estimation for Arbitrary
    Waveforms With Residual, Dilated Convolutional Neural Networks in Ferrite Cores.”
    <i>IEEE Transactions on Power Electronics</i> 40, no. 2 (2025): 3326–35. <a href="https://doi.org/10.1109/TPEL.2024.3488174">https://doi.org/10.1109/TPEL.2024.3488174</a>.'
  ieee: 'W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, and O. Wallscheid,
    “HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual,
    Dilated Convolutional Neural Networks in Ferrite Cores,” <i>IEEE Transactions
    on Power Electronics</i>, vol. 40, no. 2, pp. 3326–3335, 2025, doi: <a href="https://doi.org/10.1109/TPEL.2024.3488174">10.1109/TPEL.2024.3488174</a>.'
  mla: 'Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation
    for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in
    Ferrite Cores.” <i>IEEE Transactions on Power Electronics</i>, vol. 40, no. 2,
    2025, pp. 3326–35, doi:<a href="https://doi.org/10.1109/TPEL.2024.3488174">10.1109/TPEL.2024.3488174</a>.'
  short: W. Kirchgässner, N. Förster, T. Piepenbrock, O. Schweins, O. Wallscheid,
    IEEE Transactions on Power Electronics 40 (2025) 3326–3335.
date_created: 2026-01-06T08:07:13Z
date_updated: 2026-01-06T08:08:01Z
department:
- _id: '52'
doi: 10.1109/TPEL.2024.3488174
intvolume: '        40'
issue: '2'
keyword:
- Mathematical models
- Estimation
- Data models
- Convolutional neural networks
- Accuracy
- Magnetic hysteresis
- Magnetic cores
- Temperature measurement
- Magnetic domains
- Temperature distribution
- Convolutional neural network (CNN)
- machine learning (ML)
- magnetics
page: 3326-3335
publication: IEEE Transactions on Power Electronics
status: public
title: 'HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms With Residual,
  Dilated Convolutional Neural Networks in Ferrite Cores'
type: journal_article
user_id: '83383'
volume: 40
year: '2025'
...