AI RESEARCH
Neuromorphic Parameter Estimation for Power Converter Health Monitoring Using Spiking Neural Networks
arXiv CS.LG
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ArXi:2604.15714v1 Announce Type: cross Always-on converter health monitoring demands sub-mW edge inference, a regime inaccessible to GPU-based physics-informed neural networks. This work separates spiking temporal processing from physics enforcement: a three-layer leaky integrate-and-fire SNN estimates passive component parameters while a differentiable ODE solver provides physics-consistent