光学
计算机科学
折叠(DSP实现)
人工神经网络
航程(航空)
深层神经网络
深度学习
人工智能
算法
物理
电气工程
工程类
复合材料
材料科学
作者
Chengxiang Fan,Bo Yang,Yunpeng Liu,Qianyang Zhao,Bowen Qian,Shishuang Chen
出处
期刊:Applied Optics
[The Optical Society]
日期:2022-07-15
卷期号:61 (21): 6241-6241
被引量:3
摘要
In this paper, we propose a method to automatically generate design starting points for free-form three-mirror imaging systems with different folding configurations using deep neural networks. For a given range of system parameters, a large number of datasets are automatically generated using the double seed extended curve algorithm and coded optimization. Deep neural networks are then trained using a supervised learning approach and can be used to generate good design starting points directly. The feasibility of the method is verified by designing a free-form three-mirror system with three different folding configurations. This method can significantly reduce the design time and effort for free-form imaging systems, and can be extended to complex optical systems with more optical surfaces.
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