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    Using a Galerkin Approach to Define Terrain Surfaces 

    Source: Journal of Dynamic Systems, Measurement, and Control:;2012:;volume( 134 ):;issue: 002:;page 21017
    Author(s): Heather M. Chemistruck; David Gorsich; John B. Ferris
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Terrain is the principal source of vertical excitation to the vehicle and must be accurately represented in order to correctly predict the vehicle response. Ideally, an efficient terrain surface ...
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    Possibility-Based Design Optimization Method for Design Problems With Both Statistical and Fuzzy Input Data 

    Source: Journal of Mechanical Design:;2006:;volume( 128 ):;issue: 004:;page 928
    Author(s): Liu Du; Byeng D. Youn; David Gorsich; K. K. Choi
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The reliability based design optimization (RBDO) method is prevailing in stochastic structural design optimization by assuming the amount of input data is sufficient enough to create accurate ...
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    Integration of Possibility-Based Optimization and Robust Design for Epistemic Uncertainty 

    Source: Journal of Mechanical Design:;2007:;volume( 129 ):;issue: 008:;page 876
    Author(s): Byeng D. Youn; Kyung K. Choi; David Gorsich; Liu Du
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In practical engineering applications, there exist two different types of uncertainties: aleatory and epistemic uncertainties. This study attempts to develop a robust design optimization with ...
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    Reliability-Based Design Optimization With Confidence Level for Non-Gaussian Distributions Using Bootstrap Method 

    Source: Journal of Mechanical Design:;2011:;volume( 133 ):;issue: 009:;page 91001
    Author(s): Yoojeong Noh; Kyung K. Choi; Ikjin Lee; David Gorsich; David Lamb
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: For reliability-based design optimization (RBDO), generating an input statistical model with confidence level has been recently proposed to offset inaccurate estimation of the input statistical ...
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    Sampling-Based Stochastic Sensitivity Analysis Using Score Functions for RBDO Problems With Correlated Random Variables 

    Source: Journal of Mechanical Design:;2011:;volume( 133 ):;issue: 002:;page 21003
    Author(s): Ikjin Lee; David Gorsich; K. K. Choi; Yoojeong Noh; Liang Zhao
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This study presents a methodology for computing stochastic sensitivities with respect to the design variables, which are the mean values of the input correlated random variables. Assuming that ...
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    Improving Identifiability in Model Calibration Using Multiple Responses 

    Source: Journal of Mechanical Design:;2012:;volume( 134 ):;issue: 010:;page 100909
    Author(s): Paul D. Arendt; Daniel W. Apley; Wei Chen; David Lamb; David Gorsich
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In physics-based engineering modeling, the two primary sources of model uncertainty, which account for the differences between computer models and physical experiments, are parameter uncertainty ...
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