计算机科学
人工神经网络
集合(抽象数据类型)
过程(计算)
人工智能
深度学习
光学
领域(数学)
计算机视觉
物理
数学
操作系统
程序设计语言
纯数学
作者
Tong Yang,Dewen Cheng,Yongtian Wang
出处
期刊:Optics Express
[The Optical Society]
日期:2019-06-04
卷期号:27 (12): 17228-17228
被引量:32
摘要
In this paper, we propose a framework of starting points generation for freeform reflective triplet using back-propagation neural network based deep-learning. The network is trained using various system specifications and the corresponding surface data obtained by system evolution as the data set. Good starting points of specific system specifications for further optimization can be generated immediately using the obtained network in general. The feasibility of this design process is validated by designing the Wetherell-configuration freeform off-axis reflective triplet. The amount of time and human effort as well as the dependence on advanced design skills are significantly reduced. These results highlight the powerful ability of deep learning in the field of freeform imaging optical design.
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