Pages that link to "Item:Q2150265"
From MaRDI portal
The following pages link to A multiscale, data-driven approach to identifying thermo-mechanically coupled laws -- bottom-up with artificial neural networks (Q2150265):
Displaying 8 items.
- Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks (Q2060125) (← links)
- An FE-DMN method for the multiscale analysis of thermomechanical composites (Q2133887) (← links)
- Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) (Q2160403) (← links)
- Multiscale approach for the thermomechanical analysis of hierarchical structures (Q2920749) (← links)
- \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
- Incompressible rubber thermoelasticity: a neural network approach (Q6101617) (← links)
- Physics-informed machine-learning model of temperature evolution under solid phase processes (Q6159327) (← links)
- Elasticity-mechanics-informed generative adversarial networks for predicting the thermal strain of thermal barrier coatings penetrated by CaO-MgO-\(\mathrm{Al_2O}_3\)-\(\mathrm{SiO}_2\) (Q6163041) (← links)
