most citedHARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores

2 citations · 2 across the 2 of their papers we have counts for

collaborators

2 papers

eess.SY2026

RHINO-MAG: Recursive H-Field Inference based on Observed Magnetic Flux Density under Dynamic Excitation

Hendrik Vater, Oliver Schweins, Lukas Hölsch +3

Driven by the MagNet Challenge 2025 (MC2), increased research interest is directed towards modeling transient magnetic fields within ferrite materials. An accurate time-resolved an…

eess.SY2024★ 2 cited

HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores

Wilhelm Kirchgässner, Nikolas Förster, Till Piepenbrock +2

The MagNet Challenge 2023 calls upon competitors to develop data-driven models for the material-specific, waveform-agnostic estimation of steady-state power losses in toroidal ferr…