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相关概念视频

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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用于模拟多层薄膜的嵌套深度传输学习

Rohit Unni1,2, Kan Yao1,2, Yuebing Zheng1,2

  • 1Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA.

Advanced photonics
|August 21, 2025
PubMed
概括

通过对日益复杂的结构进行训练,嵌套转移学习显著减少了对纳米光子学的数据需求. 这种方法可以准确地预测复杂的薄膜堆的光学特性.

科学领域:

  • 纳米光子学
  • 计算材料科学
  • 机器学习应用

背景情况:

  • 机器学习 (ML) 在纳米光子学中越来越多地用于预测光学特性和设计结构.
  • 通过模拟获取复杂纳米光子结构的训练数据在计算上昂贵且耗时.
  • 传统的转移学习有好处,但对更复杂的任务有局限性.

研究的目的:

  • 引入一种新的嵌套转移学习方法,以克服纳米光子学中的数据采集挑战.
  • 能够准确地建模比以前更高的光学复杂度的薄膜堆.
  • 减少纳米光子学预测模型训练的数据要求.

主要方法:

  • 一个嵌套的转移学习策略被开发出来,在越来越复杂的结构上连续训练模型.
  • 为了预测光学属性,用于前向模型的双向循环神经网络.
  • 在设计结构的反向模型中采用了卷积混合密度网络.
  • 每个层都有轻松的材料选择,以提高灵活性.

主要成果:

  • 嵌套转移学习模型在检索复杂的任意光谱方面取得了高精度.
  • 这些模型成功地匹配了特定应用的理想光谱,例如选择性热发射器.
  • 这种方法表明能够处理具有明显更高光学复杂性的薄膜堆.
关键词:
人工神经网络反向设计多层结构纳米光子学转移学习

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  • 培训的数据要求保持温和,显示了数据效率的提高.

    • 结论:

    • 拟议的嵌套转移学习方法有效地解决了纳米光子学有限的培训数据的挑战.
    • 这种方法可以准确设计和预测复杂的纳米光子结构.
    • 这项技术为加速纳米光子研究和开发提供了有前途的多功能解决方案.