MagLearn 2: High-Fidelity, Saturation-Aware, and History-Efficient Sequence-to-sequence Modeling of Transient B-H Behaviour Under PWM Excitation

arXiv:2610.03504 · eess.SY, cs.SY · Submitted 2026-10-02 · Read on arXiv

eess.SY, cs.SY

Submitted: 2026-10-02

Updated: 2026-10-02

Comments: Submitted to IEEE Transactions on Power Electronics (TPEL) for peer review. An earlier conference version of this work was published in IEEE Xplore: https://ieeexplore.ieee.org/document/11597291/

Code: https://github.com/minjiechen/magnetchallenge-2

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 1× in this paper · explained in 2 episodes

Sources

• RHINO-MAG: Recursive H-Field Inference based on Observed Magnetic Flux Density under Dynamic Excitation
• Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition
• Neural Machine Translation by Jointly Learning to Align and Translate

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