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Article
Materials Science, Multidisciplinary
Nanxuan Wu et al.
Summary: The breakthroughs of transformation optics and metamaterials have led to the study of modern invisibility cloak. Among the various methodologies, metasurface cloak stands out due to its thinness, easy fabrication, and low loss. However, it has limitations such as convex shape, narrow bandwidth, and small incident angle. A new global inverse design is introduced to overcome these limits by using a tandem neural network to establish a bidirectional channel between the cloak and its electromagnetic response. The results show improved output accuracy, wider bandwidth, and larger incident angle, which is a significant advancement in generalizing metasurface cloak for intelligent meta-devices.
ADVANCED OPTICAL MATERIALS
(2023)
Article
Chemistry, Multidisciplinary
Yuetian Jia et al.
Summary: Optical illusion has always been of great interest due to its self-protection ability. Transformation optics, which allows manipulation of light for invisibility cloaking and optical illusion, has faced challenges due to material requirements and computational cost. A novel optical illusion based on form-free metasurfaces and deep learning architecture is proposed, reducing the parameter space and bringing illusion strategies closer to practical applications.
ADVANCED FUNCTIONAL MATERIALS
(2022)
Article
Multidisciplinary Sciences
Chao Qian et al.
Summary: This paper introduces the concept of neuro-metamaterials, which enable a dynamic entirely-optical object recognition and mirage. The experiments demonstrate that this technology can perceive the postures of rabbits at the speed of light and create dynamic optical mirages using holographic videos.
NATURE COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Qingze Tan et al.
Progress in Electromagnetics Research-PIER
(2022)
Article
Materials Science, Multidisciplinary
Jie Zhang et al.
Summary: With the rapid growth of intelligent metasurfaces, deep learning has emerged as a new method for simulating and designing metasurfaces. In this study, the authors propose a heterogeneous transfer learning approach for efficient and data-driven metasurface design, and demonstrate its effectiveness through various scenarios. They also envision a global metasurface gene bank to facilitate further research and applications.
ADVANCED OPTICAL MATERIALS
(2022)
Article
Physics, Applied
Zhixiang Fan et al.
Summary: Deep learning is a powerful data-driven force for transforming the discovery, design, and utilization of photonics and metasurfaces. There is increasing interest in deep learning-enabled on-demand structural design, which can overcome the limitations of conventional design methods. However, collecting training data for high-dimensional scatterers is challenging and costly. In this study, a transfer-learning-assisted inverse metasurface design method is proposed to alleviate the data dilemma. Experimental results demonstrate the effectiveness of the method in achieving fast far-field customization.
PHYSICAL REVIEW APPLIED
(2022)
Article
Multidisciplinary Sciences
Zhixiang Fan et al.
Summary: This study introduces the concept of homeostatic neuro-metasurfaces for automatic and monolithic management of dynamic wireless channels. Through the development of a flexible deep learning paradigm, the accuracy of global inverse design for large-scale metasurfaces exceeds 90%.
Review
Optics
Sergey Krasikov et al.
Summary: In recent years, the intersection of photonics, machine learning, and artificial intelligence has seen a significant boost in research. A new methodology has been developed to describe various photonic systems, enabling intelligent design of photonic devices. Artificial intelligence and machine learning have rapidly penetrated the fundamental physics of light and provide effective tools for studying the field of metaphotonics. This article provides an overview of the evaluation of metaphotonics induced by artificial intelligence and summarizes the concepts of machine learning with specific examples in metasystems and metasurfaces.
OPTO-ELECTRONIC ADVANCES
(2022)
Review
Optics
Wei Ma et al.
Summary: Innovative approaches and tools, particularly deep learning, are shaping the field of photonics by offering efficient means to design photonic structures and providing data-driven solutions complementary to traditional physics-based methods. The progress in deep-learning-based photonic design is promising, with various model architectures showing potential applications in specific photonic tasks.
Review
Nanoscience & Nanotechnology
Jiaqi Jiang et al.
Summary: This review discusses the importance of neural networks in photonic-system modelling and highlights the functionalities that deep neural networks can achieve, as well as the suitability of photonic systems for machine learning. Additionally, the application of fundamental data-science concepts within the context of photonics is explored.
NATURE REVIEWS MATERIALS
(2021)
Article
Optics
Zheng Zhen et al.
Summary: Being invisible has been a dream for centuries, and recent breakthroughs in technology have led to the development of a transmitted metasurface cloak, opening up new possibilities for transparent invisibility.
PHOTONICS RESEARCH
(2021)
Article
Physics, Applied
Chao Qian et al.
Summary: The topic of invisibility has long been of interest, but practical challenges remain. Combining metamaterials with deep learning shows promise as a way to address these challenges and develop intelligent, adaptive invisibility cloaks for dynamic environments. Future directions include further development in this area and the use of deep learning for other practice-oriented metadevices.
APPLIED PHYSICS LETTERS
(2021)
Article
Multidisciplinary Sciences
Ruichao Zhu et al.
Summary: This paper introduces a fast and accurate method for designing functional metasurfaces based on transfer learning, which can generate metasurface patterns monolithically for specific functions with high design accuracy. The proposed inverse design paradigm provides a efficient way for functional metasurface design and can be applied to establish a meta-atom library with full phase span.
NATURE COMMUNICATIONS
(2021)
Article
Optics
Kun Liao et al.
Summary: The rapid development of information technology has led to a growing demand for ultrafast and ultralow-energy-consumption computing. A new all-optical computing framework based on convolutional neural networks has been developed to achieve ultrafast and ultralow-energy-consumption computing using photons as information carriers. This approach shows promise for equation solving, logic operations, and other mathematical tasks, paving the way for on-chip all-optical computing.
OPTO-ELECTRONIC ADVANCES
(2021)
Article
Automation & Control Systems
Zhedong Wang et al.
Summary: A universal detection approach driven by an intelligent antenna array was introduced, utilizing machine learning to process time-varying signals and achieve simultaneous localization of frequency, direction of arrival, and polarization. Both simulation and experimental results demonstrated high accuracy and fast detection time, without the need for complex beamforming networks or trial-and-error solutions.
ADVANCED INTELLIGENT SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Peter R. Wiecha et al.
Article
Optics
Chao Qian et al.
LIGHT-SCIENCE & APPLICATIONS
(2020)
Article
Optics
Chao Qian et al.
Article
Nanoscience & Nanotechnology
Haoran Ren et al.
NATURE NANOTECHNOLOGY
(2020)
Article
Physics, Multidisciplinary
Chao Qian et al.
PHYSICAL REVIEW LETTERS
(2019)
Article
Multidisciplinary Sciences
Nasim Mohammadi Estakhri et al.
Article
Multidisciplinary Sciences
Lianlin Li et al.
NATURE COMMUNICATIONS
(2019)
Article
Optics
Min Jia et al.
LIGHT-SCIENCE & APPLICATIONS
(2019)
Article
Chemistry, Multidisciplinary
Jiaqi Jiang et al.
Article
Chemistry, Multidisciplinary
Li Gao et al.
ADVANCED MATERIALS
(2019)
Article
Multidisciplinary Sciences
John Peurifoy et al.
Article
Chemistry, Multidisciplinary
Zhaocheng Liu et al.
Article
Biotechnology & Applied Microbiology
Michael Wainberg et al.
NATURE BIOTECHNOLOGY
(2018)
Article
Materials Science, Multidisciplinary
Tong Cai et al.
ADVANCED OPTICAL MATERIALS
(2017)
Article
Multidisciplinary Sciences
Kristof T. Schuett et al.
NATURE COMMUNICATIONS
(2017)
Review
Multidisciplinary Sciences
M. I. Jordan et al.
Review
Chemistry, Physical
Nanfang Yu et al.
Review
Chemistry, Physical
Huanyang Chen et al.
Article
Geochemistry & Geophysics
Gerhard Krieger et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2008)
