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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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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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优化固体微针设计:一种全面的ML增强DOE方法

Ahmed Choukri Abdullah1, Erfan Ahmadinejad1, Savas Tasoglu1,2,3,4,5

  • 1Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkiye.

ACS measurement science au
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概括
此摘要是机器生成的。

微针 (MNs) 为药物输送和监测提供了一种最小侵入性的替代方案. 这项研究优化了使用机器学习的四个MN设计,以在生理条件下提高耐用性.

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科学领域:

  • 生物材料工程 生物材料工程
  • 机械工程 机械工程
  • 计算建模 计算建模

背景情况:

  • 微针 (MNs) 是微米尺度的针,用于药物输送,护肤和健康监测.
  • 多种针头提供了一个极少侵入性的,无痛的,低成本的替代品,用于皮下针头,减少组织损伤.

研究的目的:

  • 探索和优化四种不同的MN设计:圆,圆,方形金字塔和三角形金字塔.
  • 通过设计优化,增强各种生理条件的MN耐用性.

主要方法:

  • 在压力,曲和曲负载条件下研究了四种MN形状.
  • 在ANSYS工作台中使用了实验设计方法来分析几何参数 (底部/尖端尺寸,高度,缩角度).
  • 开发数学和响应表面模型,整合机器学习 (ML) 以实现多目标优化.

主要成果:

  • 建立了基于变形,曲负载,安全系数 (FOS) 和曲应力来评估MN性能的综合框架.
  • 生成回归模型,灵敏度图表和响应曲线以指导优化.
  • 确定了四个MN形状的优化几何设计.

结论:

  • 该研究提出了一种新的ML增强优化框架,用于微针设计.
  • 这些发现为创建适合各种生理应用的耐用MN提供了宝贵的见解.
  • 优化的MN设计可以在各种负载场景下确保结构完整性.