{"ID":2898913,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03144","arxiv_id":"2507.03144","title":"Neural Substitute Solver for Efficient Edge Inference of Power Electronic Hybrid Dynamics","abstract":"Advancing the dynamics inference of power electronic systems (PES) to the real-time edge-side holds transform-ative potential for testing, control, and monitoring. How-ever, efficiently inferring the inherent hybrid continu-ous-discrete dynamics on resource-constrained edge hardware remains a significant challenge. This letter pro-poses a neural substitute solver (NSS) approach, which is a neural-network-based framework aimed at rapid accurate inference with significantly reduced computational costs. Specifically, NSS leverages lightweight neural networks to substitute time-consuming matrix operation and high-order numerical integration steps in traditional solvers, which transforms sequential bottlenecks into highly parallel operation suitable for edge hardware. Experimental vali-dation on a multi-stage DC-DC converter demonstrates that NSS achieves 23x speedup and 60% hardware resource reduction compared to traditional solvers, paving the way for deploying edge inference of high-fidelity PES dynamics.","short_abstract":"Advancing the dynamics inference of power electronic systems (PES) to the real-time edge-side holds transform-ative potential for testing, control, and monitoring. How-ever, efficiently inferring the inherent hybrid continu-ous-discrete dynamics on resource-constrained edge hardware remains a significant challenge. Thi...","url_abs":"https://arxiv.org/abs/2507.03144","url_pdf":"https://arxiv.org/pdf/2507.03144v1","authors":"[\"Jialin Zheng\",\"Haoyu Wang\",\"Yangbin Zeng\",\"Han Xu\",\"Di Mou\",\"Hong Li\",\"Sergio Vazquez\",\"Leopoldo G. Franquelo\"]","published":"2025-07-03T19:52:32Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.LG\"]","methods":"[]","has_code":false}