发表机构
College of Computing, National University of Defense Technology; National Supercomputer Center in Tianjin; Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology（国防科技大学计算机学院; 国家超级计算天津中心; 国防科技大学前沿装备数字化软件实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CAHR-Net，通过条件自适应磁滞重构网络结合结构化条件调制与匹配优化，以仅1874个参数实现磁芯损耗建模的最优精度，性能优于同类方法。
AI 中文摘要
磁芯损耗源于磁滞回线：每个激励周期的耗散能量等于回线面积，频率、温度和波形形状通过改变回线几何形状来设定损耗。现有大多数模型仅让这些条件作用于终端标量——经验方程将其纳入拟合指数，数据驱动预测器将其附加到编码特征中——因此不存在供条件改变的中间磁滞表示。本文提出CAHR-Net，即条件自适应磁滞重构网络，其在物理作用的位置注入运行条件。该网络保留从磁通密度波形到磁场重构、回线面积积分再到功率损耗估计的可解释链，并使用按特征维度的线性调制（feature-wise linear modulation）将频率、温度和波形统计量注入中间重构表示。此外，还报告了基于AdamW、余弦调度和分阶段重构到功率损耗目标的匹配大批次训练协议，因为调制通路仅在该协议内生效。在MagNet最终A-E材料协议上，CAHR-Net仅用1874个参数就达到了6.89%的平均p95相对误差，在所有对比方法中最低；与最强的黑箱解决方案相比，其参数减少约48倍，同时最难材料的最差材料p95更低；它将物理重构主干的平均p95从7.47%降至6.89%，将最难的D材料的p95从16.40%降至14.87%。消融和条件切片分析将改进归因于物理回线重构、结构化条件调制与匹配优化轨迹的耦合。
英文摘要
Magnetic core loss originates in the hysteresis loop: the energy dissipated per excitation cycle equals the loop area, and frequency, temperature, and waveform shape set the loss by reshaping the loop geometry. Most existing models let these conditions act only on a terminal scalar - empirical equations fold them into fitted exponents, and data-driven predictors append them to encoded features - so no intermediate hysteresis representation remains for the conditions to reshape. This paper proposes CAHR-Net, a condition-adaptive hysteresis reconstruction network that injects the operating conditions where they physically act. It preserves the interpretable chain from flux density waveform to magnetic field reconstruction, loop-area integration, and power loss estimation, and uses feature-wise linear modulation to inject frequency, temperature, and waveform statistics into the intermediate reconstruction representation. A matched large-batch training protocol based on AdamW, cosine scheduling, and a staged reconstruction-to-power-loss objective is also reported, because the modulation pathway takes effect only within it. On the MagNet final A-E material protocol, CAHR-Net attains an average p95 relative error of 6.89% with only 1874 parameters, the lowest among all compared methods, together with a lower worst-material p95 than the strongest black-box solution at about 48x fewer parameters; it reduces the average p95 of the physical reconstruction backbone from 7.47% to 6.89% and the p95 of material D, the most difficult material, from 16.40% to 14.87%. Ablation and condition-slice analyses attribute the improvement to the coupling of physical loop reconstruction, structured condition modulation, and the matched optimization trajectory.
Comments10 pages, 6 figures, 5 tables
