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    Stress-Strain Modeling of Sands Using Artificial Neural Networks 

    Source: Journal of Geotechnical Engineering:;1995:;Volume ( 121 ):;issue: 005
    Author(s): G. W. Ellis; C. Yao; R. Zhao; D. Penumadu
    Publisher: American Society of Civil Engineers
    Abstract: An attempt has been made to implement artificial neural networks (ANNs) for modeling the stress-strain relationship of sands with varying grain size distribution and stress history. A series of undrained triaxial compression ...
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    Representation of Modes of Variability in Six U.S. Climate Models 

    Source: Journal of Climate:;2020:;volume( 33 ):;issue: 017:;page 7591
    Author(s): Orbe, Clara;Van Roekel, Luke;Adames, Ángel F.;Dezfuli, Amin;Fasullo, John;Gleckler, Peter J.;Lee, Jiwoo;Li, Wei;Nazarenko, Larissa;Schmidt, Gavin A.;Sperber, Kenneth R.;Zhao, Ming
    Publisher: American Meteorological Society
    Abstract: We compare the performance of several modes of variability across six U.S. climate modeling groups, with a focus on identifying robust improvements in recent models [including those participating in phase 6 of the Coupled ...
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