Pages that link to "Item:Q2952866"

The following pages link to Computational homogenization of nonlinear elastic materials using neural networks (Q2952866):

Displaying 50 items.

• Latent map Gaussian processes for mixed variable metamodeling (Q2246360) (← links)
• Predicting the mechanical properties of biopolymer gels using neural networks trained on discrete fiber network data (Q2246386) (← links)
• A deep energy method for finite deformation hyperelasticity (Q2292258) (← links)
• Functional approximation and projection of stored energy functions in computational homogenization of hyperelastic materials: a probabilistic perspective (Q2308733) (← links)
• A physics-constrained data-driven approach based on locally convex reconstruction for noisy database (Q2309342) (← links)
• Data-driven multiscale finite element method: from concurrence to separation (Q2309379) (← links)
• Deep material network with cohesive layers: multi-stage training and interfacial failure analysis (Q2309395) (← links)
• A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality (Q2309861) (← links)
• Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials (Q2310223) (← links)
• Clustering discretization methods for generation of material performance databases in machine learning and design optimization (Q2319387) (← links)
• A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites (Q2319390) (← links)
• Derivation of heterogeneous material laws via data-driven principal component expansions (Q2319393) (← links)
• Non-intrusive data learning based computational homogenization of materials with uncertainties (Q2322971) (← links)
• Recent advances on topology optimization of multiscale nonlinear structures (Q2359612) (← links)
• A nonlinear manifold-based reduced order model for multiscale analysis of heterogeneous hyperelastic materials (Q2375093) (← 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)
• Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
• Machine learning-enabled self-consistent parametrically-upscaled crystal plasticity model for Ni-based superalloys (Q2679302) (← links)
• Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
• Application of a Hopfield type neural network to the analysis of elastic problems with unilateral constraints (Q2710661) (← links)
• Modeling of materials with fading memory using neural networks (Q3549786) (← links)
• Neural network‐based parameter estimation for non‐linear finite element analyses (Q4241313) (← links)
• Computational Homogenization Using Convolutional Neural Networks (Q5051078) (← links)
• Systematic study of homogenization and the utility of circular simplified representative volume element (Q5132384) (← links)
• A neural network tool for identifying the material parameters of a finite deformation viscoplasticity model with static recovery (Q5956800) (← 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)
• A micromechanics‐based recurrent neural networks model for path‐dependent cyclic deformation of short fiber composites (Q6062835) (← links)
• A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling (Q6069980) (← links)
• Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints (Q6070057) (← links)
• <scp>Data</scp>‐physics driven reduced order homogenization (Q6071405) (← links)
• Machine learning based asymptotic homogenization and localization: Predictions of key local behaviors of multiscale configurations bearing microstructural varieties (Q6071413) (← links)
• Computational modeling and data‐driven homogenization of knitted membranes (Q6089263) (← links)
• Automatic generation of interpretable hyperelastic material models by symbolic regression (Q6092117) (← links)
• Two-stage 2D-to-3d reconstruction of realistic microstructures: implementation and numerical validation by effective properties (Q6097657) (← links)
• \(\mathrm{FE^{ANN}}\): an efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining (Q6101611) (← links)
• A data-driven harmonic approach to constructing anisotropic damage models with a minimum number of internal variables (Q6104317) (← links)
• On the micromechanics of deep material networks (Q6115410) (← links)
• A reduced order model for geometrically parameterized two-scale simulations of elasto-plastic microstructures under large deformations (Q6118519) (← links)
• A monolithic hyper ROM \(\mathrm{FE}^2\) method with clustered training at finite deformations (Q6118589) (← links)
• Pre-trained transformer model as a surrogate in multiscale computational homogenization framework for elastoplastic composite materials subjected to generic loading paths (Q6121691) (← links)
• Concurrent multiscale simulations of nonlinear random materials using probabilistic learning (Q6125499) (← links)
• Review of solution methodologies for structural analysis of composites (Q6141102) (← links)
• A time-adaptive FE\(^2\)-approach within the method of vertical lines (Q6143647) (← links)
• Efficient multiscale modeling of heterogeneous materials using deep neural networks (Q6159331) (← links)
• Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation (Q6171158) (← links)
• A Neural Network Approach for Homogenization of Multiscale Problems (Q6178099) (← links)
• On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learning (Q6186258) (← links)
• A framework for neural network based constitutive modelling of inelastic materials (Q6194141) (← links)