Pages that link to "Item:Q2039069"
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The following pages link to Multiresolution clustering analysis for efficient modeling of hierarchical material systems (Q2039069):
Displaying 19 items.
- Direct numerical simulations in solid mechanics for understanding the macroscale effects of microscale material variability (Q1800210) (← links)
- Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics (Q2083129) (← links)
- Concurrent \(n\)-scale modeling for non-orthogonal woven composite (Q2086043) (← links)
- Adaptivity for clustering-based reduced-order modeling of localized history-dependent phenomena (Q2138765) (← links)
- Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space (Q2145130) (← links)
- Self-consistent clustering analysis for multiscale modeling at finite strains (Q2174143) (← links)
- Accelerated scale bridging with sparsely approximated Gaussian learning (Q2222967) (← links)
- Self-consistent clustering analysis: an efficient multi-scale scheme for inelastic heterogeneous materials (Q2309199) (← links)
- Data-driven multiscale finite element method: from concurrence to separation (Q2309379) (← links)
- A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality (Q2309861) (← links)
- Clustering discretization methods for generation of material performance databases in machine learning and design optimization (Q2319387) (← links)
- Computational stochastic homogenization of heterogeneous media from an elasticity random field having an uncertain spectral measure (Q2667302) (← links)
- Machine learning-enabled self-consistent parametrically-upscaled crystal plasticity model for Ni-based superalloys (Q2679302) (← links)
- Model-free data-driven identification algorithm enhanced by local manifold learning (Q2692900) (← links)
- Morphology based domain partitioning of multi-phase materials: a preprocessor for multi-scale modelling (Q3587797) (← links)
- Extended tensor decomposition model reduction methods: training, prediction, and design under uncertainty (Q6120135) (← links)
- Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis (Q6159317) (← links)
- Convolution hierarchical deep-learning neural network (C-HiDeNN) with graphics processing unit (GPU) acceleration (Q6164268) (← links)
- Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation (Q6171158) (← links)
