Materials science requires the collection and analysis of large quantities of data. These data almost invariably require various post-acquisition computation in order to remove noise, classify observations, fit parametric models, or perform other operations. Recently developed machine learning (ML) algorithms have demonstrated great capability for performing many of these operations, and often produce higher quality output than traditional methods. However, it has been widely observed that such algorithms often suffer from issues such as limited generalizability and the tendency “over fit” to the input data. In order to address such issues, this work introduces a metacomputing framework capable of systematically selecting, tuning, and training the best available machine learning model in order to process an input dataset. In addition, a unique “cross training” methodology is used to incorporate underlying physics or multi-physics relationships into the structure of the resultant ML model. This metacomputing approach is demonstrated on four example problems: repairing “gaps” in a multiphysics dataset, improving the output of electron back-scatter detection (EBSD) crystallographic measurements, removing spurious artifacts from X-ray microtomography (XCMT) data, and identifying material constitutive relationships from tensile test data. The performance of the metacomputing framework on these disparate problems is discussed, as are future plans for further deploying metacomputing technologies in the context of materials science and mechanical engineering.

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