通过形态信息的神经网络预测elasto-plastic多孔介质的压力-应变行为
W Lindqwister1,2, J Peloquin3, L E Dalton4
1Department of Civil and Environmental Engineering, Duke University, Durham, USA. wlindqwister@tudelft.nl.
Communications engineering
|April 18, 2025
概括
本研究介绍了一种机器学习 (ML) 框架,使用Minkowski函数来预测多孔材料的机械强度. 这种方法为工程应用的传统测试方法提供了一个更快,数据驱动的替代方案.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 由于复杂的结构,多孔介质带来了重大的工程挑战.
- 传统的机械行为评估方法耗时且资源密集.
- 机器学习 (ML) 提供了一种数据驱动的方法来预测材料属性.
研究的目的:
- 开发一种用于预测多孔材料强度的自动化方法.
- 使用形态描述符为ML模型建立强大的特征空间.
- 为了加快分析各种多孔材料的应力-应变行为.
主要方法:
- 利用尺度形态描述符,特别是Minkowski函数,来描述多孔结构.
- 进行了单轴压缩实验,以生成应力-张力数据.
- 从形态描述符中训练了一种机器学习模型来预测压力-应变曲线.
主要成果:
- 开发了一种用于预测多孔材料强度的自动化框架.
- 证明了Minkowski函数作为ML的描述性和简要特征的有效性.
- 成功训练了一个ML模型来预测压力-应变行为.
结论:
- 拟议的ML框架可以有效地预测多孔材料的机械行为.
- 敏科夫斯基函数提供了一种有效的方法来描述ML的复杂多孔架构.
- 这种方法为预测多孔材料中更广泛的机械行为奠定了基础.
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