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Data Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics
2021
Mathematical and Computational Applications
Classification algorithms have recently found applications in computational physics for the selection of numerical methods or models adapted to the environment and the state of the physical system. For such classification tasks, labeled training data come from numerical simulations and generally correspond to physical fields discretized on a mesh. Three challenging difficulties arise: the lack of training data, their high dimensionality, and the non-applicability of common data augmentation
doi:10.3390/mca26010017
fatcat:vxbz2gqwtzh3zhkzimzt5c4wti