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    Physics-Informed Neural Networks for Missing Physics Estimation in Cumulative Damage Models: A Case Study in Corrosion Fatigue 

    Source: Journal of Computing and Information Science in Engineering:;2020:;volume( 020 ):;issue: 006:;page 061007-1
    Author(s): Dourado, Arinan; Viana, Felipe A. C.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: We present a physics-informed neural network modeling approach for missing physics estimation in cumulative damage models. This hybrid approach is designed to merge physics-informed and data-driven layers within deep neural ...
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    A Nonstationary Uncertainty Model and Bayesian Calibration of Strain-Life Models 

    Source: Journal of Verification, Validation and Uncertainty Quantification:;2021:;volume( 006 ):;issue: 001:;page 011002-1
    Author(s): Dourado, Arinan; Irmak, Firat; Viana, Felipe A. C.; Gordon, Ali P.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The Coffin–Manson–Basquin–Haford (CMBH) model is a well-accepted strain-life relationship to model fatigue life as a function of applied strain. In this paper, we propose a nonstationary uncertainty model for the CMBH ...
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