Pages that link to "Item:Q2020836"
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The following pages link to Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network (Q2020836):
Displaying 13 items.
- Semi-inverse Monte Carlo reconstruction of two-phase heterogeneous material using two-point functions (Q373750) (← links)
- Porous structure reconstruction using convolutional neural networks (Q1622858) (← links)
- New algorithms for virtual reconstruction of heterogeneous microstructures (Q1986207) (← links)
- Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
- A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
- Stochastic reconstruction of 3D microstructures from 2D cross-sectional images using machine learning-based characterization (Q2072752) (← links)
- Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics (Q2083129) (← links)
- The correlation between statistical descriptors of heterogeneous materials (Q2237445) (← links)
- Uncertainty quantification of microstructure variability and mechanical behavior of additively manufactured lattice structures (Q2237791) (← links)
- Stochastic (re)constructions of non-stationary material structures: using ensemble averaged correlation functions and non-uniform phase distributions (Q2683256) (← links)
- Microstructure reconstruction using entropic descriptors (Q3076714) (← links)
- Hierarchical reconstruction of 3D well-connected porous media from 2D exemplars using statistics-informed neural network (Q6094706) (← links)
- Deep learning and multi-level featurization of graph representations of microstructural data (Q6159319) (← links)
