Pages that link to "Item:Q2237428"
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The following pages link to Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428):
Displaying 8 items.
- Two-stage data-driven homogenization for nonlinear solids using a reduced order model (Q1788176) (← links)
- Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: extension to geometrical parameterizations (Q2096859) (← links)
- Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks (Q2190108) (← links)
- Stabilization of generalized empirical interpolation method (GEIM) in presence of noise: a novel approach based on Tikhonov regularization (Q2678500) (← links)
- Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate (Q2693414) (← links)
- Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data (Q6049615) (← links)
- Multiscale modeling of linear elastic heterogeneous structures via localized model order reduction (Q6082617) (← links)
- A reduced order model for geometrically parameterized two-scale simulations of elasto-plastic microstructures under large deformations (Q6118519) (← links)
