[PDF][PDF] Adaptive optics based on machine learning: a review
Y Guo, L Zhong, L Min, J Wang, Y Wu… - Opto-Electronic …, 2022 - researching.cn
Adaptive optics techniques have been developed over the past half century and routinely
used in large ground-based telescopes for more than 30 years. Although this technique has …
used in large ground-based telescopes for more than 30 years. Although this technique has …
Tempestas ex machina: a review of machine learning methods for wavefront control
J Fowler, R Landman - Techniques and Instrumentation for …, 2023 - spiedigitallibrary.org
As we look to the next generation of adaptive optics systems, now is the time to develop and
explore the technologies that will allow us to image rocky Earth-like planets; wavefront …
explore the technologies that will allow us to image rocky Earth-like planets; wavefront …
[PDF][PDF] 基于机器学习的激光自适应光学技术研究进展
程涛, 郭思成, 王宁, 赵孟孟, 王帅… - Chinese Journal of …, 2023 - researching.cn
摘要高功率激光是自适应光学的重要应用领域, 通过控制高功率激光系统实现高光束质量的激光
输出是激光自适应光学技术的一项重要目标. 自适应光学主要是利用波前传感器和波前校正器来 …
输出是激光自适应光学技术的一项重要目标. 自适应光学主要是利用波前传感器和波前校正器来 …
Adaptive optics control with multi-agent model-free reinforcement learning
We present a novel formulation of closed-loop adaptive optics (AO) control as a multi-agent
reinforcement learning (MARL) problem in which the controller is able to learn a non-linear …
reinforcement learning (MARL) problem in which the controller is able to learn a non-linear …
Self-optimizing adaptive optics control with reinforcement learning for high-contrast imaging
R Landman, SY Haffert… - Journal of …, 2021 - spiedigitallibrary.org
Current and future high-contrast imaging instruments require extreme adaptive optics
systems to reach contrasts necessary to directly imaged exoplanets. Telescope vibrations …
systems to reach contrasts necessary to directly imaged exoplanets. Telescope vibrations …
Integrating supervised and reinforcement learning for predictive control with an unmodulated pyramid wavefront sensor for adaptive optics
We propose a novel control approach that combines offline supervised learning to address
the challenges posed by non-linear phase reconstruction using unmodulated pyramid …
the challenges posed by non-linear phase reconstruction using unmodulated pyramid …
Focal plane wavefront sensing using machine learning: performance of convolutional neural networks compared to fundamental limits
G Orban De Xivry, M Quesnel… - Monthly Notices of …, 2021 - academic.oup.com
Focal plane wavefront sensing (FPWFS) is appealing for several reasons. Notably, it offers
high sensitivity and does not suffer from non-common path aberrations (NCPAs). The price …
high sensitivity and does not suffer from non-common path aberrations (NCPAs). The price …
Cascade adaptive optics: contrast performance analysis of a two-stage controller by numerical simulations
N Cerpa-Urra, M Kasper, C Kulcsár… - Journal of …, 2022 - spiedigitallibrary.org
The contrast performance of current extreme adaptive optics (XAO) systems can be
improved by adding a second AO correction stage featuring its own wavefront sensor (WFS) …
improved by adding a second AO correction stage featuring its own wavefront sensor (WFS) …
Highly robust spatiotemporal wavefront prediction with a mixed graph neural network in adaptive optics
The time-delay problem, which is introduced by the response time of hardware for
correction, is a critical and non-ignorable problem of adaptive optics (AO) systems. It will …
correction, is a critical and non-ignorable problem of adaptive optics (AO) systems. It will …
Performance of a U-Net-based neural network for predictive adaptive optics
We apply a U-Net-based convolutional neural network (NN) architecture to the problem of
predictive adaptive optics (AO) for tracking and imaging fast-moving targets, such as …
predictive adaptive optics (AO) for tracking and imaging fast-moving targets, such as …