Daria Mesbah , Henry Proudhon , Lionel Gélébart , David Ryckelynck
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引用次数: 0
Abstract
Crystal Plasticity FFT-based simulations, commonly used to predict crystal lattice rotation fields in polycrystalline materials, are computationally intensive. Reducing the input volume resolution can speed up these simulations, but often at the expense of prediction accuracy. To address this issue, we introduce a Multimodal EDSR (MEDSR) architecture, based on Enhanced Deep Residual Networks for Single Image Super-Resolution (EDSR). MEDSR incorporates multimodal inputs, combining low-resolution lattice rotation fields with high-resolution grain boundary distance fields, a key morphological attribute. The training process includes a gradient-based loss function that emphasizes high-gradient regions, ensuring sharper reconstructions in areas with significant mechanical variation, particularly near grain boundaries. By leveraging morphological data, MEDSR surpasses existing upsampling methods in key quality metrics.
期刊介绍:
Computer Methods in Applied Mechanics and Engineering stands as a cornerstone in the realm of computational science and engineering. With a history spanning over five decades, the journal has been a key platform for disseminating papers on advanced mathematical modeling and numerical solutions. Interdisciplinary in nature, these contributions encompass mechanics, mathematics, computer science, and various scientific disciplines. The journal welcomes a broad range of computational methods addressing the simulation, analysis, and design of complex physical problems, making it a vital resource for researchers in the field.