Fiber laser development enabled by machine learning: review and prospect

Jiang, Min; Wu, Hanshuo; An, Yi; Hou, Tianyue; Chang, Qi; Huang, Liangjin; Li, Jun; Su, Rongtao; Zhang, Pu

doi:10.1186/s43074-022-00055-3

Cited by 65 publications

(19 citation statements)

References 152 publications

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“…The output modal content can be decomposed numerically by the method of MD based on the SPGD algorithm [49,50] . The output beam profiles of the Raman laser at 1134 nm are captured at the focal plane by a CCD camera.…”

Section: Modal Decomposition On Modal Contentmentioning

confidence: 99%

Hundred-watt level high-order mode all-fiberized random distributed feedback Raman fiber laser with high mode purity

Hao

et al. 2023

Chin. Opt. Lett.

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An all-fiberized random distributed feedback Raman fiber laser (RRFL) with LP 11 mode output at 1134 nm has been demonstrated experimentally, where an intracavity acoustically induced fiber grating is employed for modal switching. The maximum output power of LP 11 mode is 93.8 W with the modal purity of 82%, calculated by numerical mode decomposition technology based on stochastic parallel-gradient descent algorithm. To our best knowledge, this is the highest output power with high purity of LP 11 mode generated from the RRFL. This work may pave a path towards advanced fiber lasers with special temporal and spatial characteristics for applications.

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Section: Modal Decomposition On Modal Contentmentioning

confidence: 99%

Hundred-watt level high-order mode all-fiberized random distributed feedback Raman fiber laser with high mode purity

Hao

et al. 2023

Chin. Opt. Lett.

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“…The mode information carried by laser beam is the key to understand intrinsic properties and transmission characteristics of HPFLs, which are challenging to be completely analyzed even with complicated measurement methods [489,492,493]. However, it should be noted that the emerging machine learning technology is expected to provide an effective and accurate technical scheme for online analysis [488][489][490][491][492][493][494][495][496].…”

Section: Summary and Prospectsmentioning

confidence: 99%

Functional Fibers and Functional Fiber-Based Components for High-Power Lasers

et al. 2022

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The success of high-power fiber lasers is fueled by maturation of active and passive fibers, combined with the availability of high-power fiber-based components. In this contribution, we first overview the enormous potential of rare-earth doped fibers in spectral coverage and recent developments of key fiber-based components employed in high-power laser systems. Subsequently, the emerging functional active and passive fibers in recent years, which exhibit tremendous advantages in balancing or mitigating parasitic nonlinearities hindering high-power transmission, are outlined from the perspectives of geometric and material engineering. Finally, novel functional applications of conventional fiber-based components for nonlinear suppression or spatial mode selection, and correspondingly, the high-power progress of function fiber-based components in power handling are introduced, which suggest more flexible controllability on high-power laser operations. Graphical abstract

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“…The advent of increased computing power has facilitated the successful application of deep learning techniques across diverse domains, including computer vision, natural language processing, medical image analysis, and material inspection [12,13]. Recently, deep learning approaches have been applied to image transmission and wavefront shaping through MMFs [14]. The convolutional neural network (CNN) has been used for learning the nonlinear relationship between the input light fields and the output specklegram pattern [15].…”

Section: Introductionmentioning

confidence: 99%

Imaging through a multimode optical fiber with principal component analysis and a variational autoencoder

Yue,

Che,

2024

J. Opt.

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Imaging through the multi-mode fiber becomes an attractive approach for gaining visual access to confined spaces. However, current imaging techniques through a multi-mode fiber(MMF) still encounter challenges including modal dispersion, complex wave-front shaping mechanism, and expensive light sources and modulations. This work proposed a cost-efficient setup with three light-emitting diodes as the illumination light source (including red, green, and blue light) and a hybrid model including the principal component analysis and a variational auto-encoder(PCAVAE) for reconstructing the transmitted images. The reconstructed images demonstrate high fidelity compared with their ground truth images. The average similarity index value of the reconstructed images is as high as 0.99. Experimental works indicated that the proposed approach was capable of rejecting 10\% white noise in the imaging process. The proposed triple-color illumination method paves a cost-effective way of transmitting images through an MMF. The PCAVAE model established in this work demonstrates great potential for processing scrambled images transmitted by the MMF. 

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Fiber laser development enabled by machine learning: review and prospect

Cited by 65 publications

References 152 publications

Hundred-watt level high-order mode all-fiberized random distributed feedback Raman fiber laser with high mode purity

Hundred-watt level high-order mode all-fiberized random distributed feedback Raman fiber laser with high mode purity

Functional Fibers and Functional Fiber-Based Components for High-Power Lasers

Imaging through a multimode optical fiber with principal component analysis and a variational autoencoder

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