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.

翻译：2023年MagNet挑战赛要求参赛者开发数据驱动模型，用于环形铁氧体磁芯稳态功率损耗的材料特定、波形无关估计。本文提出的HARDCORE方法（基于残差扩张卷积神经网络的铁氧体磁芯任意波形H场与功率损耗估计）表明，在预先训练观测数据的基础上，配备物理信息扩展的残差卷积神经网络能高效完成该任务。核心解决方案之一是建立中间模型层，该层首先重构BH曲线，再基于曲线面积估算功率损耗，使所提拓扑结构具备物理可解释性。此外，着重采用基于专家知识的特征工程与高信息含量输入，以实现精简的模型架构。模型针对每种材料从头训练，但保持拓扑结构不变。同时论证了模型规模与估计精度间的帕累托权衡，当参数低至1755个时，对于样本充足的极端材料，其相对误差的95百分位数可降至8%以下。