Machine Learning Assisted Long-Range Wireless Power Transfer
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908360294531072 |
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| author | Wang, Likai Wang, Yuqian Hu, Shengyu Li, Yunhui Chen, Hong Wang, Ce Guo, Zhiwei |
| author_facet | Wang, Likai Wang, Yuqian Hu, Shengyu Li, Yunhui Chen, Hong Wang, Ce Guo, Zhiwei |
| contents | Near-field magnetic resonance wireless power transfer (WPT) technology has garnered significant attention due to its broad application prospects in medical implants, electric vehicles, and robotics. Addressing the challenges faced by traditional WPT systems in frequency optimization and sensitivity to environmental disturbances, this study innovatively applies the gradient descent optimization algorithm to enhance a system with topological characteristics. Experimental results demonstrate that the machine learning-optimized Su-Schrieffer-Heeger (SSH)-like chain exhibits exceptional performance in transfer efficiency and system robustness. This achievement integrates non-Hermitian physics, topological physics, and machine learning, opening up new avenues and showcasing immense potential for the development of high-performance near-field wave functional devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07494 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning Assisted Long-Range Wireless Power Transfer Wang, Likai Wang, Yuqian Hu, Shengyu Li, Yunhui Chen, Hong Wang, Ce Guo, Zhiwei Applied Physics Near-field magnetic resonance wireless power transfer (WPT) technology has garnered significant attention due to its broad application prospects in medical implants, electric vehicles, and robotics. Addressing the challenges faced by traditional WPT systems in frequency optimization and sensitivity to environmental disturbances, this study innovatively applies the gradient descent optimization algorithm to enhance a system with topological characteristics. Experimental results demonstrate that the machine learning-optimized Su-Schrieffer-Heeger (SSH)-like chain exhibits exceptional performance in transfer efficiency and system robustness. This achievement integrates non-Hermitian physics, topological physics, and machine learning, opening up new avenues and showcasing immense potential for the development of high-performance near-field wave functional devices. |
| title | Machine Learning Assisted Long-Range Wireless Power Transfer |
| topic | Applied Physics |
| url | https://arxiv.org/abs/2505.07494 |