A Deep Mixture Density Network for On-Demand Inverse Design of Thin Film Reflectors

被引：0
|
作者：
Unni, Rohit [1 ,2 ]
Yao, Kan [1 ,2 ]
Zheng, Yuebing [1 ,2 ]
机构：
[1] Univ Texas Austin, Walker Dept Mech Engn, Austin, TX 78712 USA
[2] Univ Texas Austin, Texas Mat Inst, Austin, TX 78712 USA
来源：
2021 CONFERENCE ON LASERS AND ELECTRO-OPTICS (CLEO) | 2021年
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暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
We report a mixture density neural network trained for on-demand inverse design of thin film reflectors, able to retrieve accurate designs and independently reproduce conventional design methods based on physical principles. (c) 2021 The Author(s)
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页数：2
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