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相关概念视频

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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基于集体学习的离散元素方法中微观参数的智能校准方法.

Yifan Jiang1,2, Jiapeng Pu1,2, Junfeng Sun1,2

  • 1School of Civil Engineering, Southwest Jiaotong University, Chengdu, 610031, China.

Scientific reports
|October 7, 2025
PubMed
概括

本研究介绍了一种高效的堆叠集团学习模型,用于在破碎岩石质量模拟中校准块离散元素方法 (BDEM) 的微观参数. 该方法准确地预测了宏观岩石的特性,增强了工程应用.

关键词:
区块离散元素方法 区块离散元素方法相关性分析是一项相关性分析.微观参数 微观参数堆叠集体学习 堆叠集体学习

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科学领域:

  • 地质技术工程 地质技术工程
  • 计算力学 计算力学 计算力学
  • 机器学习应用 机器学习应用

背景情况:

  • 区块离散元素方法 (BDEM) 对于建模破裂岩石质量至关重要.
  • 准确的BDEM模拟需要精确的微观参数,这些参数很难直接从宏观测试中获得.
  • 传统的校准方法效率低下,计算密集.

研究的目的:

  • 开发一种高效准确的方法,用于在BDEM模拟中校准微观参数.
  • 为了建立微观参数和宏观岩石行为之间的相关性.
  • 根据实验数据验证拟议的方法.

主要方法:

  • 为离散块元素随机生成微观参数.
  • 开发和验证各种岩石力学测试的计算模型 (无轴压缩,巴西裂变,三轴压缩).
  • 构建宏观微观参数数据集和相关性分析.
  • 堆叠组合学习模型的应用和优化.

主要成果:

  • 堆叠组合模型在预测宏观岩石特性方面表现出高准确度.
  • 对单轴压力 (0.6%),弹性模量 (6.6%),间接拉伸强度 (10.6%),摩擦角度 (8.6%) 和凝聚力 (5.1%) 实现了较低的预测误差.
  • 该模型的模拟结果与实验值密切匹配,证实了其可靠性.

结论:

  • 提出的堆叠集体学习方法提供了一个非常准确和可靠的方法来预测离散元素的微观参数.
  • 与传统技术相比,这种方法显著提高了BDEM校准的效率.
  • 这些发现为涉及破碎岩石质量的实际工程应用提供了宝贵的支持.