Deep neural networks for large deformation of photo-thermo-pH responsive cationic gels

Hengdi Su; Huixian Yan; Zheng Zhong

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Deep neural networks for large deformation of photo-thermo-pH responsive cationic gels

机译：深色神经网络，用于光热-P响应阳离子凝胶的大变形

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In this work, a model is developed to analyze homogeneous and inhomogeneous large deformation of photo-thermo-pH responsive cationic gels. Constitutive equations are achieved by considering the equilibrium thermodynamics of swelling gels through vari-ational method. Employing this model, coupling effects of light intensity, temperature and pH variations on large deformation of gels are analyzed. The simulation results are compared with available experimental data. Then deep neural networks are developed to approximate solutions to equilibrium equations of inhomogeneous swelling of spherical shell structure gels. The volume phase transition temperature of the gels and their dependence on light intensity are also demonstrated.

机译：在这项工作中，开发了一种模型来分析光热-P响应阳离子凝胶的均匀和不均匀的大变形。通过考虑通过变化方法的溶胀凝胶的平衡热力学来实现构成方程。采用该模型，分析了光强度，温度和pH变形的光强度，温度和pH变形的耦合效应。将仿真结果与可用的实验数据进行比较。然后开发了深度神经网络，以近似于球形壳结构凝胶的膨松肿胀均衡方程的近似解。还证明了凝胶的体积相转变温度及其对光强度的依赖性。

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来源
《Applied Mathematical Modelling》 |2021年第12期|549-563|共15页
作者
Hengdi Su; Huixian Yan; Zheng Zhong;
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作者单位

College of Information and Management Science Henan Agricultural University Zhengzhou 450002 China;

School of Mechanical and Electrical Engineering Sanming University Sanming 365004 China;

School of Science Harbin Institute of Technology Shenzhen 518055 China;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类
关键词
Deep neural networks; Photo-thermo-pH responsive; Cationic gels; Spherical shell structure; Inhomogeneous large deformation;

机译：深神经网络;光热 - pH响应;阳离子;球形壳结构;不均匀的大变形;

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Deep neural networks for large deformation of photo-thermo-pH responsive cationic gels

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