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Predicting Products: Substitution vs. Elimination02:52

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A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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基于机器学习的多元件吸收材料的性能优化工程.

Yuhao Liu1,2, Xiaoxiao Huang1,2, Xu Yan3

  • 1State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin 150001, China.

ACS applied materials & interfaces
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PubMed
概括
此摘要是机器生成的。

性能优化工程加速了多组件微波吸收 (MA) 材料的设计. 这种方法有效地识别了基于Ni的新型材料,在多个频段中具有出色的MA特性.

关键词:
麦克斯韦尔-加内特模型机器学习是机器学习.微波吸收方式 微波吸收方式多元组件材料是多元组件材料.性能优化 性能优化 性能优化

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科学领域:

  • 材料科学 材料科学 材料科学
  • 电磁学 电磁学 电磁学 电磁学
  • 机器学习应用 机器学习应用

背景情况:

  • 与单个组件替代品相比,多组件材料提供优越的微波吸收 (MA) 性能.
  • 传统的多元MA材料设计受到复杂,高维的设计空间和依赖半经验规则的阻碍.
  • 现有的方法很难有效地探索最佳MA材料性能的巨大可能性.

研究的目的:

  • 开发一种性能优化工程方法,以加快多元件MA材料的设计.
  • 通过稀疏的数据,克服传统设计规则在高维空间中的局限性.
  • 识别具有目标性能特征的基于Ni的新型多元件MA材料.

主要方法:

  • 实现了一个闭环系统,将机器学习与扩展的麦克斯韦尔-加内特模型和电磁计算集成在一起.
  • 利用实验反来完善设计过程并验证材料性能.
  • 根据所需的MA性能,从一个几乎无限的设计空间中选和识别材料.

主要成果:

  • 成功设计了符合特定MA性能要求的Ni表面@碳纤维 (NiF) 和基于NiF的多元组件 (NMC) 材料.
  • 实现了X波段和Ku波段的目标MA性能,其超薄厚度分别为2.0mm和1.78mm.
  • 正如预测的那样,在S,C和所有频段 (2.0-18.0 GHz) 实现了MA目标.

结论:

  • 性能优化工程为设计先进的多元件MA材料提供了有效的策略.
  • 拟议的方法可以有效地探索广的材料设计空间,以量身定制MA属性.
  • 这种方法促进了高性能微波吸收材料的实际应用.