Abstract:A variant of the U-Net convolutional neural network architecture is proposed to estimate linear elastic compatibility stresses in α-Zr (hcp) polycrystalline grain structures. Training data was generated using VGrain software with a regularity α of 0.73 and uniform random orientation for the grain structures and ABAQUS to evaluate the stress fields using the finite element method. The initial dataset contains 200 samples with 20 held from training for validation. The network gives speedups of around 200x to 600… Show more
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