Pages that link to "Item:Q1788176"
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The following pages link to Two-stage data-driven homogenization for nonlinear solids using a reduced order model (Q1788176):
Displaying 24 items.
- Constraint energy minimizing generalized multiscale finite element method for nonlinear poroelasticity and elasticity (Q782019) (← links)
- Anisotropic hyperelastic constitutive models for finite deformations combining material theory and data-driven approaches with application to cubic lattice metamaterials (Q2033662) (← links)
- NTFA-enabled goal-oriented adaptive space-time finite elements for micro-heterogeneous elastoplasticity problems (Q2160410) (← links)
- A nonlinear data-driven reduced order model for computational homogenization with physics/pattern-guided sampling (Q2175074) (← links)
- Data-driven reduced homogenization for transient diffusion problems with emergent history effects (Q2236925) (← links)
- Cell division in deep material networks applied to multiscale strain localization modeling (Q2237423) (← links)
- Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method (Q2237428) (← links)
- A neural network-aided Bayesian identification framework for multiscale modeling of nanocomposites (Q2237439) (← links)
- A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
- Generation of energy-minimizing point sets on spheres and their application in mesh-free interpolation and differentiation (Q2305559) (← links)
- A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites (Q2319390) (← links)
- Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks (Q2667309) (← links)
- Nonlinear multiscale modeling of thin composite shells at finite deformations (Q2670371) (← links)
- Machine learning-enabled self-consistent parametrically-upscaled crystal plasticity model for Ni-based superalloys (Q2679302) (← links)
- Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data (Q6049615) (← links)
- Accelerated offline setup of homogenized microscopic model for multi‐scale analyses using neural network with knowledge transfer (Q6060946) (← links)
- Material modeling for parametric, anisotropic finite strain hyperelasticity based on machine learning with application in optimization of metamaterials (Q6061746) (← links)
- Surrogate modeling for the homogenization of elastoplastic composites based on RBF interpolation (Q6096506) (← links)
- Concurrent multiscale simulations of nonlinear random materials using probabilistic learning (Q6125499) (← links)
- A clustering-enhanced potential-based reduced order homogenization framework for nonlinear heterogeneous materials (Q6141162) (← links)
- Many-scale finite strain computational homogenization via concentric interpolation (Q6553434) (← links)
- Cluster based nonuniform transformation field analysis: an efficient homogenization for inelastic heterogeneous materials (Q6554081) (← links)
- FE-LSTM: a hybrid approach to accelerate multiscale simulations of architectured materials using recurrent neural networks and finite element analysis (Q6588352) (← links)
- Unsupervised machine learning classification for accelerating \(\mathrm{FE}^2\) multiscale fracture simulations (Q6641844) (← links)
