Search Results - "Liu, Dianjing"

Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures by Liu, Dianjing, Tan, Yixuan, Khoram, Erfan, Yu, Zongfu

“…Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from…”
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A Bidirectional Deep Neural Network for Accurate Silicon Color Design by Gao, Li, Li, Xiaozhong, Liu, Dianjing, Wang, Lianhui, Yu, Zongfu

“…Silicon nanostructure color has achieved unprecedented high printing resolution and larger color gamut than sRGB. The exact color is determined by localized…”
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Deep Neural Networks: A Bidirectional Deep Neural Network for Accurate Silicon Color Design (Adv. Mater. 51/2019) by Gao, Li, Li, Xiaozhong, Liu, Dianjing, Wang, Lianhui, Yu, Zongfu

“…In article number 1905467, to avoid time‐consuming electromagnetic simulation and an iterative optimization process, Li Gao, Zongfu Yu, and co‐workers report…”
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Inverse Design of Metasurfaces Based on Coupled-Mode Theory and Adjoint Optimization by Zhou, Ming, Liu, Dianjing, Belling, Samuel W, Cheng, Haotian, Kats, Mikhail A, Fan, Shanhui, Povinelli, Michelle L, Yu, Zongfu

“…Metasurfaces typically have sizes much larger than the wavelength yet contain a large number of subwavelength features. Thus, it is difficult to design entire…”
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The Application of Machine Learning for Designing and Controlling Electromagnetic Fields by Liu, Dianjing

“…Machine Learning is the study of computer algorithms that improve automatically through experience. In contrary to rule-based artificial intelligence which…”
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Self-Focused Thermal Emission and Holography Realized by Mesoscopic Thermal Emitters by Zhou, Ming, Khoram, Erfan, Liu, Dianjing, Liu, Boyuan, Fan, Shanhui, Povinelli, Michelle L, Yu, Zongfu

“…Controlling thermal emission plays a vital role in various applications. Existing control of thermal emissions have been limited to simple functions such as…”
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Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures by Liu, Dianjing, Tan, Yixuan, Khoram, Erfan, Yu, Zongfu

“…We demonstrate a tandem neural network architecture that tolerates inconsistent training instances in inverse design of nanophotonic devices. It provides a way…”
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Training deep neural networks for the inverse design of nanophotonic structures by Liu, Dianjing, Tan, Yixuan, Khoram, Erfan, Yu, Zongfu

“…Data inconsistency leads to a slow training process when deep neural networks are used for the inverse design of photonic devices, an issue that arises from…”
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Nonlinear Nanophotonic Media for Artificial Neural Computing by Khoram, Erfan, Chen, Ang, Liu, Dianjing, Wang, Qiqi, Yuan, Ming, Yu, Zongfu

“…We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Such a medium exploits sub-wavelength linear and nonlinear…”
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Optimization of Nonlinear Nanophotonic Media for Artificial Neural Inference by Khoram, Erfan, Chen, Ang, Liu, Dianjing, Wang, Qiqi, Yuan, Ming, Yu, Zongfu

“…We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Such a medium exploits linear and nonlinear scatterers to…”
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Nanophotonic Media for Artificial Neural Inference by Khoram, Erfan, Chen, Ang, Liu, Dianjing, Ying, Lei, Wang, Qiqi, yuan, Ming, Yu, Zongfu

“…We show optical waves passing through a nanophotonic medium can perform artificial neural computing. Complex information, is encoded in the wave front of an…”
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Examination of pairs in neutrino mixing matrix by Liu, Dianjing, Ma, Bo-Qiang

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Examination of pairs in neutrino mixing matrix by Liu, Dianjing, Ma, Bo-Qiang

“…Phys.Rev. D92 (2015) 3, 033011 We exam the pairs of neutrino mixing matrix and suggest pairs that can be used in the construction of new mixing patterns, with…”
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