Pages that link to "Item:Q4462927"

The following pages link to Numerical implementation of a neural network based material model in finite element analysis (Q4462927):

Displaying 50 items.

• Accurate cyclic plastic analysis using a neural network material model (Q597632) (← links)
• Artificial neural networks in numerical modelling of composites (Q649421) (← links)
• Self-learning finite elements for inverse estimation of thermal constitutive models (Q981413) (← links)
• Artificial neural network as an incremental nonlinear constitutive model for a finite element code. (Q1420917) (← links)
• Global flexibility simulation and element stiffness simulation in finite element analysis with neural network (Q1574289) (← links)
• High efficient load paths analysis with U* index generated by deep learning (Q1986717) (← links)
• Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
• A DNN-based data-driven modeling employing coarse sample data for real-time flexible multibody dynamics simulations (Q2020773) (← links)
• Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures (Q2020954) (← links)
• Deep learned finite elements (Q2021024) (← links)
• Finite element solver for data-driven finite strain elasticity (Q2021912) (← links)
• Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (Q2021962) (← links)
• A kernel method for learning constitutive relation in data-driven computational elasticity (Q2024599) (← links)
• Neural network constitutive model for crystal structures (Q2033624) (← links)
• Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step (Q2072735) (← links)
• Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks (Q2115570) (← links)
• Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning (Q2120033) (← links)
• The mixed deep energy method for resolving concentration features in finite strain hyperelasticity (Q2134762) (← links)
• On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745) (← links)
• A multiscale, data-driven approach to identifying thermo-mechanically coupled laws -- bottom-up with artificial neural networks (Q2150265) (← links)
• Designing phononic crystal with anticipated band gap through a deep learning based data-driven method (Q2176922) (← links)
• Variational framework for distance-minimizing method in data-driven computational mechanics (Q2184276) (← links)
• A machine learning based plasticity model using proper orthogonal decomposition (Q2184326) (← links)
• A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths (Q2236174) (← links)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
• Material behavior modeling with multi-output support vector regression (Q2282402) (← links)
• Computational mechanics enhanced by deep learning (Q2310108) (← links)
• Derivation of heterogeneous material laws via data-driven principal component expansions (Q2319393) (← links)
• Multiscale modeling of concrete. From mesoscale to macroscale (Q2443846) (← links)
• Characterizing rate-dependent material behaviors in self-learning simulation (Q2459257) (← links)
• Self-learning simulation method for inverse nonlinear modeling of cyclic behavior of connections (Q2638009) (← links)
• Constrained neural network training and its application to hyperelastic material modeling (Q2667314) (← links)
• Finite element coupled positive definite deep neural networks mechanics system for constitutive modeling of composites (Q2670357) (← links)
• Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
• Modular machine learning-based elastoplasticity: generalization in the context of limited data (Q2693407) (← links)
• Optimization framework for calibration of constitutive models enhanced by neural networks (Q2848328) (← links)
• Neural network constitutive modelling for non-linear characterization of anisotropic materials (Q3018022) (← links)
• Modeling of materials with fading memory using neural networks (Q3549786) (← links)
• Extracting inelastic metal behaviour through inverse analysis: a shift in focus from material models to material behaviour (Q3612707) (← links)
• A new neural network-based model for hysteretic behavior of materials (Q3623126) (← links)
• Neural network‐based parameter estimation for non‐linear finite element analyses (Q4241313) (← links)
• Implicit constitutive modelling for viscoplasticity using neural networks (Q4253789) (← links)
• Approximation de calculs éléments finis par un nouveau réseau de neurones (Q4266784) (← links)
• Material Modeling via Thermodynamics-Based Artificial Neural Networks (Q5021908) (← links)
• FEA-AI and AI-AI: Two-Way Deepnets for Real-Time Computations for Both Forward and Inverse Mechanics Problems (Q5193350) (← links)
• (Q5852424) (← links)
• Seq-SVF: an unsupervised data-driven method for automatically identifying hidden governing equations (Q6051370) (← links)
• A comparative study on different neural network architectures to model inelasticity (Q6082629) (← links)
• Physically enhanced training for modeling rate-independent plasticity with feedforward neural networks (Q6084775) (← links)
• Intelligent stiffness computation for plate and beam structures by neural network enhanced finite element analysis (Q6090780) (← links)