基于工况匹配与部分最优传输的跨材料磁芯损耗预测
Cross-Material Magnetic Core Loss Prediction via Operating-Condition Matching and Partial Optimal Transport
AI总结：
本文提出基于工况支持的部分最优传输的Transformer迁移方法T-PCOT，通过工况匹配与部分最优传输实现跨材料磁芯损耗预测，在多材料对比中较基线方法显著降低了预测误差，达到最先进性能。
摘要：
跨材料磁芯损耗预测需要在有限测量数据的情况下适配新材料的响应，而全局特征对齐会忽略源域与目标域激励覆盖范围的差异，这促使研究人员选择可比样本并控制每个源测量值的对齐贡献。本文提出T-PCOT，一种基于工况支持的部分最优传输的Transformer迁移方法：频率、磁通摆幅、温度和波形描述符定义了可比工况的稀疏图；容量受限的传输计划将对齐分配到源测量值，并保留成本较高的目标质量不匹配，该计划在训练前从测量输入构建，用于指导局部波形-特征一致性；重要性加权采样方案在小批量优化期间同时保留均匀源回归和传输目标。在三对材料和三个目标标签预算上的实验表明，在全部九项对比中，T-PCOT相较于基于Transformer的多核最大均值差异（T-MKMMD），降低了平均相对误差（MRE）和第95百分位误差（P95）：宏观MRE从12.60%降至9.65%，宏观P95从30.94%降至25.06%，对应相对降幅分别为23.41%和19.01%。受控对比与 ablation 研究检验了输入处理、工况对应和源容量的作用，结果证明，通过工况支持组织跨材料对齐相较于所对比的全局对齐方法具有预测优势，该方法在磁芯损耗迁移学习中达到了最先进的预测性能。
英文摘要：
Cross-material magnetic core loss prediction requires adapting to new material responses with limited measurements. Global feature alignment overlooks differences in source and target excitation coverage. This motivates selecting comparable samples and controlling each source measurement's alignment contribution. This paper proposes T-PCOT, a Transformer transfer method based on condition-supported partial optimal transport. Frequency, flux excursion, temperature, and waveform descriptors define a sparse graph of comparable operating conditions. A capacity-constrained transport plan distributes alignment across source measurements and leaves costly target mass unmatched. The plan is constructed from measured inputs before training and guides local waveform-feature consistency. An importance-weighted sampling scheme preserves both uniform source regression and the transport objective during minibatch optimization. Experiments on three material pairs and three target label budgets show that T-PCOT reduces mean relative error (MRE) and 95th-percentile error (P95) relative to Transformer-based multiple-kernel maximum mean discrepancy (T-MKMMD) in all nine comparisons. Macro MRE decreases from 12.60% to 9.65%, and macro P95 from 30.94% to 25.06%, corresponding to relative reductions of 23.41% and 19.01%. Controlled comparisons and ablations examine the roles of input processing, condition correspondence, and source capacity. The results demonstrate the predictive advantage of organizing cross-material alignment through operating-condition support over the compared global alignment method. The proposed method achieves state-of-the-art predictive performance in magnetic core loss transfer learning.
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