Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Determination of Crystal Structures01:29

Determination of Crystal Structures

138
In the late 1800s, the revelation that light extended beyond visible wavelengths led to the discovery of X-rays by Wilhelm Roentgen. Recognized as high-energy electromagnetic radiation with short wavelengths, X-rays prompted exploration into their interaction with crystals. Max von Laue proposed in 1912 that the periodic arrangement of atoms, ions, or molecules in crystals would cause them to diffract X-rays, a hypothesis confirmed through experiments with copper sulfate and zinc sulfide...
138

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Machine Learning-Based Radiopatho-Clinical Model Integrating Ultrasound Radiomics and Kleiner Score for Prognosis Prediction in NAFLD-Related Hepatocellular Carcinoma.

Journal of hepatocellular carcinoma·2026
Same author

Annual progress in transbronchial diagnosis and treatment of pulmonary malignant tumors [2025]: a narrative review.

Translational lung cancer research·2026
Same author

Microbiota‑derived indole‑3‑propionic acid reprograms bone marrow stem cell fate via PPARγ suppression to rescue osteoporosis.

International journal of molecular medicine·2026
Same author

Gasdermin D antagonizes immunosuppression in prostate cancer by inducing LAMC2 degradation to block M2 macrophage polarization.

Journal for immunotherapy of cancer·2026
Same author

Telitacicept for refractory IgA nephropathy: a case series.

Journal of nephrology·2026
Same author

MTFR2 promotes VDAC1 oligomerization to reprogram BCAA metabolism in tumor cells to polarize TAMs in LUAD.

Cell death & disease·2026

相关实验视频

Updated: May 6, 2026

Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
10:35

Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials

Published on: September 26, 2014

12.2K

在光子晶体中预测切尔恩数,使用基于生成对抗网络的数据增强.

Ao Sun, Haotian Wu, Jingxuan Guo

    Optics express
    |January 29, 2025
    PubMed
    概括
    此摘要是机器生成的。

    深度学习与麦克斯韦方程相结合,可以准确地预测光子晶体的切尔恩数. 一种新的数据增强技术提高了预测准确性,克服了复杂物理计算中的数据限制.

    更多相关视频

    Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
    08:04

    Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

    Published on: May 27, 2020

    8.3K
    Uncovering Hidden Dynamics of Natural Photonic Structures Using Holographic Imaging
    05:45

    Uncovering Hidden Dynamics of Natural Photonic Structures Using Holographic Imaging

    Published on: March 31, 2022

    2.6K

    相关实验视频

    Last Updated: May 6, 2026

    Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials
    10:35

    Using Microwave and Macroscopic Samples of Dielectric Solids to Study the Photonic Properties of Disordered Photonic Bandgap Materials

    Published on: September 26, 2014

    12.2K
    Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids
    08:04

    Excitonic Hamiltonians for Calculating Optical Absorption Spectra and Optoelectronic Properties of Molecular Aggregates and Solids

    Published on: May 27, 2020

    8.3K
    Uncovering Hidden Dynamics of Natural Photonic Structures Using Holographic Imaging
    05:45

    Uncovering Hidden Dynamics of Natural Photonic Structures Using Holographic Imaging

    Published on: March 31, 2022

    2.6K

    科学领域:

    • 拓式光子学 拓式光子学
    • 凝聚物质物理学 凝聚物质物理学
    • 材料科学 是一种材料科学.
    • 计算物理学的计算物理.

    背景情况:

    • 切尔恩数对于理解光子晶体和光学系统的拓性质至关重要.
    • 计算切尔恩数是计算密集和耗时的,阻碍了实际应用.
    • 有限的数值数据往往对科学计算中的深度学习模型构成挑战.

    研究的目的:

    • 开发一种深度学习方法来预测二维光子晶体的切恩数.
    • 为了应对深度学习中培训数据不足的挑战,用于复杂的物理量计算.
    • 提高切尔恩数确定效率和准确性.

    主要方法:

    • 将深度学习模型与用于切尔恩数预测的麦克斯韦方程集成.
    • 实施数字对图像生成对抗网络 (GANs) 数据增强策略.
    • 应用到一个具有正方形格子结构的二维光子晶体.

    主要成果:

    • 在测试数据集上获得了92.25%的平均预测准确度.
    • 拟议的数据增强方法提高了7.95%的切尔恩数预测准确度.
    • 证明了出色的预测性能,验证了深度学习方法.

    结论:

    • 开发的深度学习方法提供了一个准确而有效的方法来预测切尔恩数.
    • 基于GANs的新型数据增强有效地克服了科学机器学习中的数据稀缺性.
    • 这种方法为涉及复杂物理计算的深度学习应用提供了有希望的解决方案,并且在其他科学领域也有潜力.