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Visual representation of the network architecture of partially input convex neural networks

Visual representation of the network architecture of partially input convex neural networks

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Figure 1: 100 grain polycrystalline domain with concomitant finite...
Figure 2: Orientation distribution functions for a single texture...
Figure 3: 72 loading vectors depicting the ratio of applied loads used...
Figure 4: Mean and bounding macroscopic yield surfaces of three...
+6 Figure 5: Initial classification-based data (a,c) and final...
Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations
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  • Jan 2022
The influence of the microstructure of a polycrystalline material on its macroscopic deformation response is still one of the major problems in materials engineering. For materials characterized by elastic-plastic deformation responses, predictive computational models to characterize crystal-plasticity (CP) have been developed. However, due to thei...
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... overview of the network architecture is given in Figure 9. Similarly to the previous case, the output is convex with respect to the inputs y c if all weights ...

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