Pages that link to "Item:Q6044224"
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The following pages link to A three-dimensional prediction method of stiffness properties of composites based on deep learning (Q6044224):
Displaying 11 items.
- Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
- A wavelet-based learning approach assisted multiscale analysis for estimating the effective thermal conductivities of particulate composites (Q2021276) (← links)
- Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter (Q2037488) (← links)
- A data-driven approach to full-field nonlinear stress distribution and failure pattern prediction in composites using deep learning (Q2145129) (← links)
- Ensemble wavelet-learning approach for predicting the effective mechanical properties of concrete composite materials (Q2171482) (← links)
- Machine learning regression approaches for predicting the ultimate buckling load of variable-stiffness composite cylinders (Q2234140) (← links)
- A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM (Q2237268) (← links)
- A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures (Q2237801) (← links)
- Transfer learning of deep material network for seamless structure-property predictions (Q2319403) (← links)
- A Mixed Wavelet-Learning Method of Predicting Macroscopic Effective Heat Transfer Conductivities of Braided Composite Materials (Q5065186) (← links)
- Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading (Q6164292) (← links)
