Xinyu Wei, Jianbing Sang, Chuan Tian, Lifang Sun, Baoyou Liu
{"title":"基于机器学习和有限元的不同类型红细胞膜本构参数","authors":"Xinyu Wei, Jianbing Sang, Chuan Tian, Lifang Sun, Baoyou Liu","doi":"10.1142/s0219876222500578","DOIUrl":null,"url":null,"abstract":"Research on mechanical response of single red blood cells (RBCs) to mechanical stimuli and the complex material properties of erythrocyte membranes is significant. This work proposes a novel procedure that combines nonlinear finite element method and two machine learning algorithms including Two-Way Deepnets and XGboost together with experiments to identify the hyper elastic material parameters of erythrocyte membranes. Finite element models were established to simulate the stretching process of erythrocyte optical tweezers with different constitutive material parameters from three constitutive models. And the results from the finite element analysis were carried out to generate the training sets for the neural networks. In order to validate the predictions in great detail, the finite element response curves based on the three groups of predicted constitutive parameters are compared with the experimental data. The comparison results show that the Two-Way Deepnets model has performed better efficiency and accuracy and that Reduced Polynomial can describe more precisely the hyperelastic properties of the erythrocyte membrane in the range of experimentally obtained characteristics of single RBCs. This research provides new insights into the identification of constitutive parameters of biological cell membranes, which is crucial for the future research on mechanical mechanisms of the biological cells.","PeriodicalId":54968,"journal":{"name":"International Journal of Computational Methods","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2022-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Different Types of Constitutive Parameters Red Blood Cell Membrane Based on Machine Learning and FEM\",\"authors\":\"Xinyu Wei, Jianbing Sang, Chuan Tian, Lifang Sun, Baoyou Liu\",\"doi\":\"10.1142/s0219876222500578\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Research on mechanical response of single red blood cells (RBCs) to mechanical stimuli and the complex material properties of erythrocyte membranes is significant. This work proposes a novel procedure that combines nonlinear finite element method and two machine learning algorithms including Two-Way Deepnets and XGboost together with experiments to identify the hyper elastic material parameters of erythrocyte membranes. Finite element models were established to simulate the stretching process of erythrocyte optical tweezers with different constitutive material parameters from three constitutive models. And the results from the finite element analysis were carried out to generate the training sets for the neural networks. In order to validate the predictions in great detail, the finite element response curves based on the three groups of predicted constitutive parameters are compared with the experimental data. The comparison results show that the Two-Way Deepnets model has performed better efficiency and accuracy and that Reduced Polynomial can describe more precisely the hyperelastic properties of the erythrocyte membrane in the range of experimentally obtained characteristics of single RBCs. This research provides new insights into the identification of constitutive parameters of biological cell membranes, which is crucial for the future research on mechanical mechanisms of the biological cells.\",\"PeriodicalId\":54968,\"journal\":{\"name\":\"International Journal of Computational Methods\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Computational Methods\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1142/s0219876222500578\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computational Methods","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1142/s0219876222500578","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Different Types of Constitutive Parameters Red Blood Cell Membrane Based on Machine Learning and FEM
Research on mechanical response of single red blood cells (RBCs) to mechanical stimuli and the complex material properties of erythrocyte membranes is significant. This work proposes a novel procedure that combines nonlinear finite element method and two machine learning algorithms including Two-Way Deepnets and XGboost together with experiments to identify the hyper elastic material parameters of erythrocyte membranes. Finite element models were established to simulate the stretching process of erythrocyte optical tweezers with different constitutive material parameters from three constitutive models. And the results from the finite element analysis were carried out to generate the training sets for the neural networks. In order to validate the predictions in great detail, the finite element response curves based on the three groups of predicted constitutive parameters are compared with the experimental data. The comparison results show that the Two-Way Deepnets model has performed better efficiency and accuracy and that Reduced Polynomial can describe more precisely the hyperelastic properties of the erythrocyte membrane in the range of experimentally obtained characteristics of single RBCs. This research provides new insights into the identification of constitutive parameters of biological cell membranes, which is crucial for the future research on mechanical mechanisms of the biological cells.
期刊介绍:
The purpose of this journal is to provide a unique forum for the fast publication and rapid dissemination of original research results and innovative ideas on the state-of-the-art on computational methods. The methods should be innovative and of high scholarly, academic and practical value.
The journal is devoted to all aspects of modern computational methods including
mathematical formulations and theoretical investigations;
interpolations and approximation techniques;
error analysis techniques and algorithms;
fast algorithms and real-time computation;
multi-scale bridging algorithms;
adaptive analysis techniques and algorithms;
implementation, coding and parallelization issues;
novel and practical applications.
The articles can involve theory, algorithm, programming, coding, numerical simulation and/or novel application of computational techniques to problems in engineering, science, and other disciplines related to computations. Examples of fields covered by the journal are:
Computational mechanics for solids and structures,
Computational fluid dynamics,
Computational heat transfer,
Computational inverse problem,
Computational mathematics,
Computational meso/micro/nano mechanics,
Computational biology,
Computational penetration mechanics,
Meshfree methods,
Particle methods,
Molecular and Quantum methods,
Advanced Finite element methods,
Advanced Finite difference methods,
Advanced Finite volume methods,
High-performance computing techniques.