High power laser is an important application field of adaptive optics, and controlling a high-power laser system to achieve high-beam-quality laser output is an important goal of laser adaptive optics technology. To achieve high beam quality throughout the transmission of high-power laser to the target, adaptive optics must simultaneously correct the aberration of the high-power laser source, thermal effect and optical element aberration in the transmission channel, and atmospheric turbulence. Currently, adaptive optics has enabled the output beam of multimode high-power lasers to achieve near-diffraction limit beam quality; for example, the beam quality of a 1-MW DF chemical laser reaches twice the diffraction limit, and the average beam quality of a 105-kW solid-state laser is about 2.9. However, when promoting the practical application of high-power lasers with different systems in different working scenarios, adaptive optics still faces the following problems: 1) The near-field intensity distribution of high-power lasers is uneven, and there are large gradient aberrations locally. 2) In the case of strong turbulence transmission, the wavefront aberration has high spatio-temporal frequency characteristics, and some information of the wavefront is dynamically missing. 3) The signal-to-noise ratio and spatial resolution of the wavefront detector will be low due to the weak light and strong noise background of the dim target. 4) Platform vibration, temperature change, and other environmental factors will cause variation of the system model parameters. To solve these problems, researchers have developed a laser adaptive optics technique based on machine learning. This paper reviews the current intelligent development of laser adaptive optics based on machine learning in wavefront restoration, wavefront prediction, phase inversion, and wavefront correction, and the potential and challenges of current research methods used in the field of high-power laser are discussed.

In this paper, the research progress of laser adaptive optics techniques based on machine learning are summarized from four aspects: wavefront reconstruction, wavefront prediction, phase inversion, and wavefront correction. Further, the potential and challenges of the current methods in the application of high-power laser are discussed. In terms of wavefront reconstruction of a Hartmann sensor, this paper introduces the improvement of a deep learning method based on the calculation process of wavefront reconstruction, such as centroid extraction, aberration coefficient calculated from wavefront slope, wavefront phase calculated from spot array image, and full aperture wavefront slope estimation calculated from partial wavefront slope information. Finally, high-precision centroid calculation under low signal-to-noise ratio is realized, as shown in Table 1. High spatial frequency aberration restoration under low spatial resolution is shown in Fig. 4. Full-aperture wavefront information presumption under light deficiency is shown in Fig. 5. In terms of wavefront prediction, a variety of wavefront prediction methods based on the improved long short term memory (LSTM) network are introduced to achieve high-precision wavefront prediction under different turbulence intensities and delay periods, as shown in Figs. 7 and 8, and the consistent prediction accuracy is still available when some parameters of the atmospheric turbulence model are changed, as shown in Fig. 6. In terms of phase inversion, deep learning-based phase inversion methods are introduced from two pairs of focal and defocusing far field images as well as a single frame image modulated from far-field images, which realizes the direct transformation of far field image information to wavefront information, as shown in Figs. 9 and 12; further, this avoids the iterative process in wavefront sensorless adaptive optics technology. In terms of wavefront correction, the dynamic description of the deep learning network for the local response relationship of the adaptive optical system and dynamic solution of the system control strategy based on reinforcement learning are introduced, as shown in Figs. 15 and 17, respectively, realizing the self-identification and self-adaptive adjustment of the adaptive optical system when the system input and model parameters are changed.

Machine learning has demonstrated excellent potential in solving multiple problems faced by high-power laser systems in laser adaptive optics technology. Its contributions can be summarized as follows: 1) Achieving high-precision wavefront detection under weak targets, strong light background, and strong turbulence effects. 2) Breaking the limitation of system delay on the control bandwidth of the adaptive optical system and improving the correction accuracy of the system for high spatio-temporal frequency aberrations. 3) Avoiding the iterative process and improving the control bandwidth of the wavefront sensorless adaptive optics system. 4) Eliminating the influence of high-power laser applied in different scenarios on the correction performance of the adaptive optical system and improving the adaptive ability of the optical system in different maneuvering platforms and working environments.

However, the practical application of current research methods in high-power laser systems still has the following problems to be studied: 1) Training sample collection. The output time of high-power lasers is limited, and its thermal effect aberration characteristics are different from atmospheric turbulence aberration characteristics. It is difficult to collect or generate training samples conforming to the characteristics of high-power laser thermal effect aberration, and under the condition of high-power laser output or strong background noise, its corresponding real wavefront aberration is difficult to obtain. 2) Training environment and duration. Compared with the astronomical observation, it is difficult for high-power laser to provide the interactive environment required by machine learning. Therefore, an interactive environment that can simulate the states of high-power laser systems should be established with the reference light. In addition, the laser beacon irradiation time is limited, and characteristics of atmospheric turbulence aberration change with time, so it is urgent for machine learning methods to have a short training time. 3) Multi-method fusion. There are many problems in the application of high-power laser, such as strong background noise, dynamic light deficiency, and high spatio-temporal aberration. Taking wavefront reconstruction as an example, in order to realize the restoration of high spatial frequency aberration under complex conditions while ensuring the accuracy and speed of wavefront restoration, the existing network methods need to be integrated and optimized.