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    Multiphysics Missing Data Synthesis: A Machine Learning Approach for Mitigating Data Gaps and Artifacts 

    Source: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 005:;page 51010-1
    Author(s): Steuben, J. C.; Geltmacher, A. B.; Rodriguez, S. N.; Graber, B. D.; Iliopoulos, A. P.; Michopoulos, J. G.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The presence of gaps and spurious nonphysical artifacts in datasets is a nearly ubiquitous problem in many scientific and engineering domains. In the context of multiphysics numerical models, data gaps may arise from lack ...
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    Machine-Learning Metacomputing for Materials Science Data 

    Source: Journal of Computing and Information Science in Engineering:;2024:;volume( 024 ):;issue: 011:;page 111005-1
    Author(s): Steuben, J. C.; Geltmacher, A. B.; Rodriguez, S. N.; Birnbaum, A. J.; Graber, B. D.; Rawlings, A. K.; Iliopoulos, A. P.; Michopoulos, J. G.
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Materials science requires the collection and analysis of great quantities of data. These data almost invariably require various post-acquisition computation to remove noise, classify observations, fit parametric models, ...
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