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The MagNet Challenge 2023 calls upon competitors to develop data-drivenmodels for the material-specific, waveform-agnostic estimation of steady-statepower losses in toroidal ferrite cores. The following HARDCORE (H-field andpower loss estimation for Arbitrary waveforms with Residual, Dilatedconvolutional neural networks in ferrite COREs) approach shows that a residualconvolutional neural network with physics-informed extensions can serve thistask efficiently when trained on observational data beforehand. One keysolution element is an intermediate model layer which first reconstructs the bhcurve and then estimates the power losses based on the curve's area renderingthe proposed topology physically interpretable. In addition, emphasis wasplaced on expert-based feature engineering and information-rich inputs in orderto enable a lean model architecture. A model is trained from scratch for eachmaterial, while the topology remains the same. A Pareto-style trade-off betweenmodel size and estimation accuracy is demonstrated, which yields an optimum atas low as 1755 parameters and down to below 8\,\% for the 95-th percentile ofthe relative error for the worst-case material with sufficient samples.

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