YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
    • View Item
    •   YE&T Library
    • ASME
    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Model Form Calibration in Drift Diffusion Simulation Using Fractional Derivatives

    Source: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 003::page 31006
    Author:
    Wang, Yan
    DOI: 10.1115/1.4032312
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In modeling and simulation, modelform uncertainty arises from the lack of knowledge and simplification during the modeling process and numerical treatment for ease of computation. Traditional uncertainty quantification (UQ) approaches are based on assumptions of stochasticity in real, reciprocal, or functional spaces to make them computationally tractable. This makes the prediction of important quantities of interest, such as rare events, difficult. In this paper, a new approach to capture modelform uncertainty is proposed. It is based on fractional calculus, and its flexibility allows us to model a family of nonGaussian processes, which provides a more generic description of the physical world. A generalized fractional Fokker–Planck equation (fFPE) is used to describe the driftdiffusion processes under longrange correlations and memory effects. A new modelcalibration approach based on the maximum mutual information is proposed to reduce modelform uncertainty, where an optimization procedure is taken.
    • Download: (829.6Kb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Model Form Calibration in Drift Diffusion Simulation Using Fractional Derivatives

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/160180
    Collections
    • ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

    Show full item record

    contributor authorWang, Yan
    date accessioned2017-05-09T01:25:29Z
    date available2017-05-09T01:25:29Z
    date issued2016
    identifier issn2332-9017
    identifier otherRISK_2_3_031006.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/160180
    description abstractIn modeling and simulation, modelform uncertainty arises from the lack of knowledge and simplification during the modeling process and numerical treatment for ease of computation. Traditional uncertainty quantification (UQ) approaches are based on assumptions of stochasticity in real, reciprocal, or functional spaces to make them computationally tractable. This makes the prediction of important quantities of interest, such as rare events, difficult. In this paper, a new approach to capture modelform uncertainty is proposed. It is based on fractional calculus, and its flexibility allows us to model a family of nonGaussian processes, which provides a more generic description of the physical world. A generalized fractional Fokker–Planck equation (fFPE) is used to describe the driftdiffusion processes under longrange correlations and memory effects. A new modelcalibration approach based on the maximum mutual information is proposed to reduce modelform uncertainty, where an optimization procedure is taken.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleModel Form Calibration in Drift Diffusion Simulation Using Fractional Derivatives
    typeJournal Paper
    journal volume2
    journal issue3
    journal titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
    identifier doi10.1115/1.4032312
    journal fristpage31006
    journal lastpage31006
    treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 003
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian