Characterising Mechanical Properties of Flowing Microcapsules Using a Deep Convolutional Neural Network
作者机构：School of Engineering and Materials ScienceQueen Mary University of LondonLondon E14NSUnited Kingdom Department of Mechanical EngineeringUniversity College LondonLondon WC1E 6BTUnited Kingdom
出 版 物：《Advances in Applied Mathematics and Mechanics》 (应用数学与力学进展（英文）)
年 卷 期：2022年第14卷第1期
页 面：79-100页
核心收录：
学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程]
主 题：Microcapsules flow cytometry deep convolutional neural network high throughput mechanical characterisation
摘 要：Deformable microcapsules are widely used in industries and also serve as a mechanical model of living biological *** this study,we develop a novel method,by integrating a deep convolutional neural network(DCNN)with high-fidelity mechanistic capsule modelling,to identify the membrane constitutive law and estimate associated parameters of a microcapsule from its steady deformed profile in a capillary *** with conventional inverse methods,the present approach is more accurate and can increase the prediction throughput rate by a few orders of *** can process capsules with large deformation in inertial ***,the method can predict the capsule membrane shear elasticity,area dilatation modulus and initial inflation from a single steady capsule *** explore the mechanism that the DCNN makes decisions by considering its feature maps,and discuss their potential implication on the development of inverse *** present method provides a promising tool which may enable high-throughput mechanical characterisation of microcapsules and biological cells in microfluidic flows.
维普期刊数据库
