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首页> 外文期刊>Advanced Materials >Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy
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Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi-Supervised Learning Strategy

机译：基于半监督学习策略的深度生成模型基于深度生成模型的超重要性概率表示及逆设计

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摘要

The research of metamaterials has achieved enormous success in the manipulation of light in a prescribed manner using delicately designed subwavelength structures, so-called meta-atoms. Even though modern numerical methods allow for the accurate calculation of the optical response of complex structures, the inverse design of metamaterials, which aims to retrieve the optimal structure according to given requirements, is still a challenging task owing to the nonintuitive and nonunique relationship between physical structures and optical responses. To better unveil this implicit relationship and thus facilitate metamaterial designs, it is proposed to represent metamaterials and model the inverse design problem in a probabilistically generative manner, enabling to elegantly investigate the complex structure-performance relationship in an interpretable way, and solve the one-to-many mapping issue that is intractable in a deterministic model. Moreover, to alleviate the burden of numerical calculations when collecting data, a semisupervised learning strategy is developed that allows the model to utilize unlabeled data in addition to labeled data in an end-to-end training. On a data-driven basis, the proposed deep generative model can serve as a comprehensive and efficient tool that accelerates the design, characterization, and even new discovery in the research domain of metamaterials, and photonics in general.
机译：使用精确设计的亚波长结构，所谓的元原子，超材料的研究在以规定的方式进行了巨大的成功。尽管现代数值方法允许精确计算复杂结构的光学响应，但超级材料的逆设计，旨在根据给定的要求检索最佳结构，由于物理之间的非线性和非野性关系，仍然是一个具有挑战性的任务结构和光学响应。为了更好地揭示这种隐式关系，从而促进超材料设计，建议以概率地生成的方式表示超级设计问题，使能以可解释的方式优化复杂的结构性能关系，并解决一个 - 到多种映射问题，即在确定性模型中是棘手的。此外，为了减轻数值计算时收集数据时的负担，开发了一个半熟的学习策略，允许模型除了在端到端培训中的标记数据外，还可以使用未标记的数据。在数据驱动的基础上，所提出的深度生成模型可以作为全面和高效的工具，可以加速超材料的研究领域和光子学研究领域的设计，表征甚至新发现。

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  • 来源
    《Advanced Materials》 |2019年第35期|1901111.1-1901111.9|共9页
  • 作者

    Ma Wei; Cheng Feng; Xu Yihao; Wen Qinlong; Liu Yongmin;

  • 作者单位

    Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA;

    Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA;

    Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA;

    Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA;

    Northeastern Univ Dept Mech & Ind Engn Boston MA 02115 USA|Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    deep learning; metamaterials; photonics;

    机译：深度学习;超材料;光子学;

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