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    Bayesian Calibration and Uncertainty Quantification for a Physics-Based Precipitation Model of Nickel–Titanium Shape-Memory Alloys 

    Source: Journal of Manufacturing Science and Engineering:;2017:;volume( 139 ):;issue: 007:;page 71002
    Author(s): Tapia, Gustavo; Johnson, Luke; Franco, Brian; Karayagiz, Kubra; Ma, Ji; Arroyave, Raymundo; Karaman, Ibrahim; Elwany, Alaa
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Uncertainty quantification (UQ) is an emerging field that focuses on characterizing, quantifying, and potentially reducing, the uncertainties associated with computer simulation models used in a wide range of applications. ...
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    Bayesian Calibration of Multiple Coupled Simulation Models for Metal Additive Manufacturing: A Bayesian Network Approach 

    Source: ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg:;2021:;volume( 008 ):;issue: 001:;page 11111-1
    Author(s): Ye, Jiahui; Mahmoudi, Mohamad; Karayagiz, Kubra; Johnson, Luke; Seede, Raiyan; Karaman, Ibrahim; Arroyave, Raymundo; Elwany, Alaa
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Modeling and simulation for additive manufacturing (AM) are critical enablers for understanding process physics, conducting process planning and optimization, and streamlining qualification and certification. It is often ...
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