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.

利用数据驱动模型对环形铁氧体芯中稳态功率损耗进行材料特定、波形不可知的估计的MagNet Challenge 2023呼吁竞争者。HARDCORE方法表明，基于物理信息扩展的残差卷积神经网络在先前的观测数据上训练后，可以高效地完成该任务。其中一个关键解决要素是中间模型层，首先对bh曲线进行重建，然后基于曲线的面积估计功率损耗，使所提出的拓扑结构在物理上可解释。此外，在专家基础的特征工程和信息丰富输入方面强调，以实现精简模型体系结构。针对每种材料从头开始训练模型，而拓扑结构保持不变。演示了模型大小和估计精度之间的帕累托样式权衡，在最不利情况下具有充分样本时，参数最低为1755，最低95%相对误差为8%。

HARDCORE：基于残差扩张卷积神经网络的磁芯中任意波形的H场和功率损耗估计