Abstract:Driven by the MagNet Challenge 2025 (MC2), increased research interest is directed towards modeling transient magnetic fields within ferrite materials. An accurate time-resolved and temperature-aware H-field prediction is essential for optimizing magnetic components in applications with quasi-stationary / non-stationary excitation waveforms. Within the scope of this investigation, a selection of model architectures with varying degrees of physically motivated structure is compared. Based on a Pareto investigation, a rather black-box gated recurrent unit (GRU) model structure with a graceful initialization setup is found to offer the most attractive model size vs. model accuracy trade-off in the small-model regime, while the examined physics-inspired models performed worse. For a GRU-based model architecture with only 325 parameters (trained separately per material), an average sequence relative error of 8.02 % and an average normalized energy relative error of 1.07 % across five different materials are achieved on unseen test data. With this excellent parameter efficiency, the proposed model won the first place in the performance category of the MC2.
From: Hendrik Vater [view email]
[v1]
Tue, 31 Mar 2026 13:43:52 UTC (1,698 KB)
[v2]
Wed, 9 Sep 2026 16:37:50 UTC (2,510 KB)
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