Bidirectional cascaded deep neural networks with a pretrained autoencoder for dielectric metasurfaces

Kong, Weichao; Chen, Jun; Huang, Zengxin; Kuang, Dengfeng

doi:10.1364/prj.428425

Cited by 12 publications

(4 citation statements)

References 36 publications

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“…Their ability of dimensional reduction makes them very convenient to efficiently use a minimal data input to reach optimal structuring and configuration. They have been applied in numerous metasurface designs [205,292,293].…”

Section: Autoencodermentioning

confidence: 99%

“…Their procedure enabled them to obtain accurate quantitative field distributions, not just qualitative ones. Other interesting works based on tandem neural networks include those that handle dielectric metasurfaces by applying a pre-trained autoencoder [292], deal with the nonuniqueness problem with a metasurface-based invisibility cloak [351], optimize nanostructure color design in a truncated cone silicon metasurface [352], etc. Du et al proposed a deep neural network model [353] consisting of a forward design part (a transposed convolutional network with dense layers for the fast determination of a metasurface optical response) and an inverse design part (convolutional neural networks with dense layers) that can automatically build a metasurface structure based on the optical response(s).…”

Section: Bidirectional Designmentioning

confidence: 99%

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Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Jakšić

2024

Photonics

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The interplay between two paradigms, artificial intelligence (AI) and optical metasurfaces, nowadays appears obvious and unavoidable. AI is permeating literally all facets of human activity, from science and arts to everyday life. On the other hand, optical metasurfaces offer diverse and sophisticated multifunctionalities, many of which appeared impossible only a short time ago. The use of AI for optimization is a general approach that has become ubiquitous. However, here we are witnessing a two-way process—AI is improving metasurfaces but some metasurfaces are also improving AI. AI helps design, analyze and utilize metasurfaces, while metasurfaces ensure the creation of all-optical AI chips. This ensures positive feedback where each of the two enhances the other one: this may well be a revolution in the making. A vast number of publications already cover either the first or the second direction; only a modest number includes both. This is an attempt to make a reader-friendly critical overview of this emerging synergy. It first succinctly reviews the research trends, stressing the most recent findings. Then, it considers possible future developments and challenges. The author hopes that this broad interdisciplinary overview will be useful both to dedicated experts and a general scholarly audience.

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Section: Autoencodermentioning

confidence: 99%

Section: Bidirectional Designmentioning

confidence: 99%

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Jakšić

2024

Photonics

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“…The BCDNN model was developed for microarray gene expression classification [19]. The DNN is separated into decoder, encoder, translator, and simulator.…”

Section: Process Involved In Bcdnn-based Classificationmentioning

confidence: 99%

Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model

et al. 2022

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Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data.

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“…The DL algorithms are employed in various fields, such as nature language processing [ 24 , 25 , 26 ], image recognition [ 27 ], finance [ 28 , 29 , 30 ], and medicine [ 31 , 32 , 33 ]. Especially for the aspect of nanophotonics, the DL approach has proved to be one of the most advanced tools to study many nonintuitive and nonlinear physics issues [ 34 , 35 , 36 , 37 ], including the problem of the inverse design of photonics devices [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ]. Peurifpy et al [ 46 ] utilized 50,000 samples to train neural networks to design a multi-layer dielectric spherical nanoparticle in 2018, which was regarded as the landmark in this field.…”

Section: Introductionmentioning

confidence: 99%

Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions

et al. 2022

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A data-enhanced deep greedy optimization (DEDGO) algorithm is proposed to achieve the efficient and on-demand inverse design of multiple transition metal dichalcogenides (TMDC)-photonic cavity-integrated heterojunctions operating in the strong coupling regime. Precisely, five types of photonic cavities with different geometrical parameters are employed to alter the optical properties of monolayer TMDC, aiming at discovering new and intriguing physics associated with the strong coupling effect. Notably, the traditional rigorous coupled wave analysis (RCWA) approach is utilized to generate a relatively small training dataset for the DEDGO algorithm. Importantly, one remarkable feature of DEDGO is the integration the decision theory of reinforcement learning, which remedies the deficiencies of previous research that focused more on modeling over decision making, increasing the success rate of inverse prediction. Specifically, an iterative optimization strategy, namely, deep greedy optimization, is implemented to improve the performance. In addition, a data enhancement method is also employed in DEDGO to address the dependence on a large amount of training data. The accuracy and effectiveness of the DEDGO algorithm are confirmed to be much higher than those of the random forest algorithm and deep neural network, making possible the replacement of the time-consuming conventional scanning optimization method with the DEDGO algorithm. This research thoroughly describes the universality, interpretability, and excellent performance of the DEDGO algorithm in exploring the underlying physics of TMDC-cavity heterojunctions, laying the foundations for the on-demand inverse design of low-dimensional material-based nano-devices.

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Bidirectional cascaded deep neural networks with a pretrained autoencoder for dielectric metasurfaces

Cited by 12 publications

References 36 publications

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Synergy between AI and Optical Metasurfaces: A Critical Overview of Recent Advances

Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model

Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions

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