Model Form Calibration in Drift Diffusion Simulation Using Fractional DerivativesSource: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 003::page 31006Author:Wang, Yan
DOI: 10.1115/1.4032312Publisher: 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.
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contributor author | Wang, Yan | |
date accessioned | 2017-05-09T01:25:29Z | |
date available | 2017-05-09T01:25:29Z | |
date issued | 2016 | |
identifier issn | 2332-9017 | |
identifier other | RISK_2_3_031006.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/160180 | |
description 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Model Form Calibration in Drift Diffusion Simulation Using Fractional Derivatives | |
type | Journal Paper | |
journal volume | 2 | |
journal issue | 3 | |
journal title | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering | |
identifier doi | 10.1115/1.4032312 | |
journal fristpage | 31006 | |
journal lastpage | 31006 | |
tree | ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering:;2016:;volume( 002 ):;issue: 003 | |
contenttype | Fulltext |