Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration
Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. By sampling the spatially separated laser array before the combiner and detuning the optical path delays, deep learning techniques are incorporated into...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | High Power Laser Science and Engineering |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2095471925000246/type/journal_article |
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| author | Hongbing Zhou Rumao Tao Xi Feng Haoyu Zhang Min Li Xiong Xin Yuyang Peng Honghuan Lin Jianjun Wang Lixin Yan Feng Jing |
| author_facet | Hongbing Zhou Rumao Tao Xi Feng Haoyu Zhang Min Li Xiong Xin Yuyang Peng Honghuan Lin Jianjun Wang Lixin Yan Feng Jing |
| author_sort | Hongbing Zhou |
| collection | DOAJ |
| description | Machine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. By sampling the spatially separated laser array before the combiner and detuning the optical path delays, deep learning techniques are incorporated into filled-aperture CBC to achieve single-step phase control. The neural network is trained with far-field diffractive patterns at the defocus plane to establish one-to-one phase-intensity mapping, and the phase prediction accuracy is significantly enhanced thanks to the strategies of sin-cos loss function and two-layer output of the phase vector that are adopted to resolve the phase discontinuity issue. The results indicate that the trained network can predict phases with improved accuracy, and phase-locking of nine-channel filled-aperture CBC has been numerically demonstrated in a single step with a residual phase of λ/70. To the best of our knowledge, this is the first time that machine learning has been made feasible in filled-aperture CBC laser systems. |
| format | Article |
| id | doaj-art-012fbc5c92b14a69b9820679100545dc |
| institution | Kabale University |
| issn | 2095-4719 2052-3289 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | High Power Laser Science and Engineering |
| spelling | doaj-art-012fbc5c92b14a69b9820679100545dc2025-08-20T03:47:23ZengCambridge University PressHigh Power Laser Science and Engineering2095-47192052-32892025-01-011310.1017/hpl.2025.24Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstrationHongbing Zhou0https://orcid.org/0009-0000-3825-3124Rumao Tao1https://orcid.org/0000-0001-9880-811XXi Feng2Haoyu Zhang3Min Li4Xiong Xin5Yuyang Peng6Honghuan Lin7Jianjun Wang8Lixin Yan9https://orcid.org/0000-0002-6572-4400Feng Jing10Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang, China Department of Engineering Physics, Tsinghua University, Beijing, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaDepartment of Engineering Physics, Tsinghua University, Beijing, ChinaLaser Fusion Research Center, China Academy of Engineering Physics, Mianyang, ChinaMachine learning has already shown promising potential in tiled-aperture coherent beam combining (CBC) to achieve versatile advanced applications. By sampling the spatially separated laser array before the combiner and detuning the optical path delays, deep learning techniques are incorporated into filled-aperture CBC to achieve single-step phase control. The neural network is trained with far-field diffractive patterns at the defocus plane to establish one-to-one phase-intensity mapping, and the phase prediction accuracy is significantly enhanced thanks to the strategies of sin-cos loss function and two-layer output of the phase vector that are adopted to resolve the phase discontinuity issue. The results indicate that the trained network can predict phases with improved accuracy, and phase-locking of nine-channel filled-aperture CBC has been numerically demonstrated in a single step with a residual phase of λ/70. To the best of our knowledge, this is the first time that machine learning has been made feasible in filled-aperture CBC laser systems.https://www.cambridge.org/core/product/identifier/S2095471925000246/type/journal_articlecoherent beam combiningmachine learningphase control |
| spellingShingle | Hongbing Zhou Rumao Tao Xi Feng Haoyu Zhang Min Li Xiong Xin Yuyang Peng Honghuan Lin Jianjun Wang Lixin Yan Feng Jing Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration High Power Laser Science and Engineering coherent beam combining machine learning phase control |
| title | Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration |
| title_full | Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration |
| title_fullStr | Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration |
| title_full_unstemmed | Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration |
| title_short | Machine learning phase control of filled-aperture coherent beam combining: principle and numerical demonstration |
| title_sort | machine learning phase control of filled aperture coherent beam combining principle and numerical demonstration |
| topic | coherent beam combining machine learning phase control |
| url | https://www.cambridge.org/core/product/identifier/S2095471925000246/type/journal_article |
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