---
_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. IEEE Transactions on Power
Electronics. 2025;40(2):3326-3335. doi:10.1109/TPEL.2024.3488174'
apa: 'Kirchgässner, W., Förster, N., Piepenbrock, T., Schweins, O., & Wallscheid,
O. (2025). HARDCORE: H-Field and Power Loss Estimation for Arbitrary Waveforms
With Residual, Dilated Convolutional Neural Networks in Ferrite Cores. IEEE
Transactions on Power Electronics, 40(2), 3326–3335. https://doi.org/10.1109/TPEL.2024.3488174'
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={10.1109/TPEL.2024.3488174},
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.”
IEEE Transactions on Power Electronics 40, no. 2 (2025): 3326–35. https://doi.org/10.1109/TPEL.2024.3488174.'
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,” IEEE Transactions
on Power Electronics, vol. 40, no. 2, pp. 3326–3335, 2025, doi: 10.1109/TPEL.2024.3488174.'
mla: 'Kirchgässner, Wilhelm, et al. “HARDCORE: H-Field and Power Loss Estimation
for Arbitrary Waveforms With Residual, Dilated Convolutional Neural Networks in
Ferrite Cores.” IEEE Transactions on Power Electronics, vol. 40, no. 2,
2025, pp. 3326–35, doi:10.1109/TPEL.2024.3488174.'
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'
...
---
_id: '62078'
abstract:
- lang: eng
text: 'Fiber reinforced plastics (FRP) exhibit strongly non-linear deformation behavior.
To capture this in simulations, intricate models with a variety of parameters
are typically used. The identification of values for such parameters is highly
challenging and requires in depth understanding of the model itself. Machine learning
(ML) is a promising approach for alleviating this challenge by directly predicting
parameters based on experimental results. So far, this works mostly for purely
artificial data. In this work, two approaches to generalize to experimental data
are investigated: a sequential approach, leveraging understanding of the constitutive
model and a direct, purely data driven approach. This is exemplary carried out
for a highly non-linear strain rate dependent constitutive model for the shear
behavior of FRP.The sequential model is found to work better on both artificial
and experimental data. It is capable of extracting well suited parameters from
the artificial data under realistic conditions. For the experimental data, the
model performance depends on the composition of the experimental curves, varying
between excellently suiting and reasonable predictions. Taking the expert knowledge
into account for ML-model training led to far better results than the purely data
driven approach. Robustifying the model predictions on experimental data promises
further improvement. '
author:
- first_name: Johannes
full_name: Gerritzen, Johannes
id: '105344'
last_name: Gerritzen
orcid: 0000-0002-0169-8602
- first_name: Andreas
full_name: Hornig, Andreas
last_name: Hornig
- first_name: Peter
full_name: Winkler, Peter
last_name: Winkler
- first_name: Maik
full_name: Gude, Maik
last_name: Gude
citation:
ama: 'Gerritzen J, Hornig A, Winkler P, Gude M. Direct parameter identification
for highly nonlinear strain rate dependent constitutive models using machine learning.
In: ECCM21 - Proceedings of the 21st European Conference on Composite Materials.
Vol 3. European Society for Composite Materials (ESCM); 2024:1252–1259. doi:10.60691/yj56-np80'
apa: Gerritzen, J., Hornig, A., Winkler, P., & Gude, M. (2024). Direct parameter
identification for highly nonlinear strain rate dependent constitutive models
using machine learning. ECCM21 - Proceedings of the 21st European Conference
on Composite Materials, 3, 1252–1259. https://doi.org/10.60691/yj56-np80
bibtex: '@inproceedings{Gerritzen_Hornig_Winkler_Gude_2024, title={Direct parameter
identification for highly nonlinear strain rate dependent constitutive models
using machine learning}, volume={3}, DOI={10.60691/yj56-np80},
booktitle={ECCM21 - Proceedings of the 21st European Conference on Composite Materials},
publisher={European Society for Composite Materials (ESCM)}, author={Gerritzen,
Johannes and Hornig, Andreas and Winkler, Peter and Gude, Maik}, year={2024},
pages={1252–1259} }'
chicago: Gerritzen, Johannes, Andreas Hornig, Peter Winkler, and Maik Gude. “Direct
Parameter Identification for Highly Nonlinear Strain Rate Dependent Constitutive
Models Using Machine Learning.” In ECCM21 - Proceedings of the 21st European
Conference on Composite Materials, 3:1252–1259. European Society for Composite
Materials (ESCM), 2024. https://doi.org/10.60691/yj56-np80.
ieee: 'J. Gerritzen, A. Hornig, P. Winkler, and M. Gude, “Direct parameter identification
for highly nonlinear strain rate dependent constitutive models using machine learning,”
in ECCM21 - Proceedings of the 21st European Conference on Composite Materials,
2024, vol. 3, pp. 1252–1259, doi: 10.60691/yj56-np80.'
mla: Gerritzen, Johannes, et al. “Direct Parameter Identification for Highly Nonlinear
Strain Rate Dependent Constitutive Models Using Machine Learning.” ECCM21 -
Proceedings of the 21st European Conference on Composite Materials, vol. 3,
European Society for Composite Materials (ESCM), 2024, pp. 1252–1259, doi:10.60691/yj56-np80.
short: 'J. Gerritzen, A. Hornig, P. Winkler, M. Gude, in: ECCM21 - Proceedings of
the 21st European Conference on Composite Materials, European Society for Composite
Materials (ESCM), 2024, pp. 1252–1259.'
date_created: 2025-11-04T12:47:06Z
date_updated: 2026-02-27T06:46:21Z
doi: 10.60691/yj56-np80
intvolume: ' 3'
keyword:
- Direct parameter identification
- Machine learning
- Convolutional neural networks
- Strain rate dependency
- Fiber reinforced plastics
- woven composites
- segmentation
- synthetic training data
- x-ray computed tomography
language:
- iso: eng
page: 1252–1259
project:
- _id: '130'
name: 'TRR 285: Methodenentwicklung zur mechanischen Fügbarkeit in wandlungsfähigen
Prozessketten'
- _id: '137'
name: TRR 285 - Subproject A03
- _id: '131'
name: TRR 285 - Project Area A
publication: ECCM21 - Proceedings of the 21st European Conference on Composite Materials
publication_identifier:
isbn:
- 978-2-912985-01-9
publisher: European Society for Composite Materials (ESCM)
status: public
title: Direct parameter identification for highly nonlinear strain rate dependent
constitutive models using machine learning
type: conference
user_id: '105344'
volume: 3
year: '2024'
...