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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: May 12, 2025

Surrogate Model Development for Digital Experiments in Welding
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在拓优化结构的后处理中,基于神经网络的替代模型.

Jude Thaddeus Persia1, Myung Kyun Sung1, Soobum Lee1

  • 1Department of Mechanical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250 USA.

Neural computing & applications
|May 8, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了深度人工神经网络 (DANN),用于对拓优化结构的精确后处理. 这种方法通过预测应力值来有效地改进设计,最大限度地降低工程应用的计算成本.

关键词:
神经网络的神经网络的神经网络参数化的参数化后期处理 后期处理替代模型的替代模型拓优化优化拓的优化风洞平衡风力道的平衡

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科学领域:

  • 工程 工程师 工程师 工程师
  • 计算力学 计算力学 计算力学
  • 人工智能的人工智能

背景情况:

  • 拓优化可以生成高效的结构,但需要进行后处理以实现可制造性.
  • 将拓优化设计转换为CAD平滑了边界,改变了应力分布,需要调和.
  • 有限元素方法 (FEM) 模拟对于代设计精细化而言是计算密集型的.

研究的目的:

  • 为后处理拓优化结构开发一个准确和高效的替代模型.
  • 为了最大限度地减少与拓优化和CAD模型之间调整应力值相关的计算费用.
  • 为了使多个应力性能指标的几何参数能够微调.

主要方法:

  • 一个feedforward深层人工神经网络 (DANN) 设计,其架构参数针对特定压力输出进行了优化.
  • DANN的训练是使用从实验设计 (DoE) 模型中生成的数据进行的,将几何维度与各种负载下的应力联系起来.
  • 使用经过训练的DANN来预测压力性能指标,构建了一个代孕模型.

主要成果:

  • 基于DANN的替代模型在组合负载条件下准确预测了高度非线性应力.
  • ·米塞斯的应力预测达到10%的准确度,轴力传感器的应力预测达到2%的准确度.
  • 该方法显著减少了为优化后处理设计所需的FEM计算的数量.

结论:

  • 提出的基于DANN的替代建模方法对后处理拓优化结构有效.
  • 这种方法为传统的代FEM基于后处理提供了一个计算效率高的替代方案.
  • 该技术通过其在风洞平衡设计的后处理中成功应用得到了验证.