标题
Inverse design of photonic topological state via machine learning
作者
关键词
-
出版物
APPLIED PHYSICS LETTERS
Volume 114, Issue 18, Pages 181105
出版商
AIP Publishing
发表日期
2019-05-08
DOI
10.1063/1.5094838
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence
- (2019) Huiying Liang et al. NATURE MEDICINE
- Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials
- (2018) Wei Ma et al. ACS Nano
- Machine Learning Topological Invariants with Neural Networks
- (2018) Pengfei Zhang et al. PHYSICAL REVIEW LETTERS
- Machine Learning Out-of-Equilibrium Phases of Matter
- (2018) Jordan Venderley et al. PHYSICAL REVIEW LETTERS
- Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures
- (2018) Dianjing Liu et al. ACS Photonics
- Nanophotonic particle simulation and inverse design using artificial neural networks
- (2018) John Peurifoy et al. Science Advances
- Generative Model for the Inverse Design of Metasurfaces
- (2018) Zhaocheng Liu et al. NANO LETTERS
- Observation of a phononic quadrupole topological insulator
- (2018) Marc Serra-Garcia et al. NATURE
- A quantized microwave quadrupole insulator with topologically protected corner states
- (2018) Christopher W. Peterson et al. NATURE
- Topolectrical-circuit realization of topological corner modes
- (2018) Stefan Imhof et al. Nature Physics
- Photonics meets topology
- (2018) Bi-Ye Xie et al. OPTICS EXPRESS
- Asymmetric topological edge states in a quasiperiodic Harper chain composed of split-ring resonators
- (2018) Zhiwei Guo et al. OPTICS LETTERS
- Mastering the game of Go without human knowledge
- (2017) David Silver et al. NATURE
- Learning phase transitions by confusion
- (2017) Evert P. L. van Nieuwenburg et al. Nature Physics
- Machine learning phases of matter
- (2017) Juan Carrasquilla et al. Nature Physics
- Quantum Loop Topography for Machine Learning
- (2017) Yi Zhang et al. PHYSICAL REVIEW LETTERS
- Quantized electric multipole insulators
- (2017) Wladimir A. Benalcazar et al. SCIENCE
- Solving the quantum many-body problem with artificial neural networks
- (2017) Giuseppe Carleo et al. SCIENCE
- Detection of Zak phases and topological invariants in a chiral quantum walk of twisted photons
- (2017) Filippo Cardano et al. Nature Communications
- Mastering the game of Go with deep neural networks and tree search
- (2016) David Silver et al. NATURE
- Robust reconfigurable electromagnetic pathways within a photonic topological insulator
- (2016) Xiaojun Cheng et al. NATURE MATERIALS
- Topological states in photonic systems
- (2016) Ling Lu et al. Nature Physics
- Colloquium: Topological band theory
- (2016) A. Bansil et al. REVIEWS OF MODERN PHYSICS
- Probabilistic machine learning and artificial intelligence
- (2015) Zoubin Ghahramani NATURE
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Determination of Zak phase by reflection phase in 1D photonic crystals
- (2015) Wen Sheng Gao et al. OPTICS LETTERS
- Machine learning: Trends, perspectives, and prospects
- (2015) M. I. Jordan et al. SCIENCE
- Topological photonics
- (2014) Ling Lu et al. Nature Photonics
- Autoencoder for words
- (2014) Cheng-Yuan Liou et al. NEUROCOMPUTING
- Multimodal integration learning of robot behavior using deep neural networks
- (2014) Kuniaki Noda et al. ROBOTICS AND AUTONOMOUS SYSTEMS
- Surface Impedance and Bulk Band Geometric Phases in One-Dimensional Systems
- (2014) Meng Xiao et al. Physical Review X
- Photonic Floquet topological insulators
- (2013) Mikael C. Rechtsman et al. NATURE
- Direct measurement of the Zak phase in topological Bloch bands
- (2013) Marcos Atala et al. Nature Physics
- Photonic topological insulators
- (2012) Alexander B. Khanikaev et al. NATURE MATERIALS
- Colloquium: Topological insulators
- (2010) M. Z. Hasan et al. REVIEWS OF MODERN PHYSICS
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