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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.03144 |
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| _version_ | 1866908433753571328 |
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| author | Zheng, Jialin Wang, Haoyu Zeng, Yangbin Xu, Han Mou, Di Li, Hong Vazquez, Sergio Franquelo, Leopoldo G. |
| author_facet | Zheng, Jialin Wang, Haoyu Zeng, Yangbin Xu, Han Mou, Di Li, Hong Vazquez, Sergio Franquelo, Leopoldo G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Substitute Solver for Efficient Edge Inference of Power Electronic Hybrid Dynamics Zheng, Jialin Wang, Haoyu Zeng, Yangbin Xu, Han Mou, Di Li, Hong Vazquez, Sergio Franquelo, Leopoldo G. Systems and Control Machine Learning 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. |
| title | Neural Substitute Solver for Efficient Edge Inference of Power Electronic Hybrid Dynamics |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2507.03144 |