  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 ferrite cores. The following HARDCORE (H-field and
power loss estimation for Arbitrary waveforms with Residual, Dilated
convolutional neural networks in ferrite COREs) approach shows that a residual
convolutional neural network with physics-informed extensions can serve this
task efficiently when trained on observational data beforehand. One key
solution element is an intermediate model layer which first reconstructs the bh
curve and then estimates the power losses based on the curve's area rendering
the proposed topology physically interpretable. In addition, emphasis was
placed on expert-based feature engineering and information-rich inputs in order
to enable a lean model architecture. A model is trained from scratch for each
material, while the topology remains the same. A Pareto-style trade-off between
model size and estimation accuracy is demonstrated, which yields an optimum at
as low as 1755 parameters and down to below 8\,\% for the 95-th percentile of
the relative error for the worst-case material with sufficient samples.
