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    A PhysicsGuided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements

    Source: Journal of Biomechanical Engineering:;2022:;volume( 144 ):;issue: 012::page 121012
    Author:
    You, Huaiqian;Zhang, Quinn;Ross, Colton J.;Lee, ChungHao;Hsu, MingChen;Yu, Yue
    DOI: 10.1115/1.4055918
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: We present a datadriven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From indistribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on outofdistribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physicsguided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the smalldeformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the datadriven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.
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      A PhysicsGuided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4288961
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    contributor authorYou, Huaiqian;Zhang, Quinn;Ross, Colton J.;Lee, ChungHao;Hsu, MingChen;Yu, Yue
    date accessioned2023-04-06T13:02:13Z
    date available2023-04-06T13:02:13Z
    date copyright10/28/2022 12:00:00 AM
    date issued2022
    identifier issn1480731
    identifier otherbio_144_12_121012.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4288961
    description abstractWe present a datadriven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From indistribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on outofdistribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physicsguided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the smalldeformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the datadriven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleA PhysicsGuided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements
    typeJournal Paper
    journal volume144
    journal issue12
    journal titleJournal of Biomechanical Engineering
    identifier doi10.1115/1.4055918
    journal fristpage121012
    journal lastpage12101211
    page11
    treeJournal of Biomechanical Engineering:;2022:;volume( 144 ):;issue: 012
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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