Pages that link to "Item:Q3018022"

The following pages link to Neural network constitutive modelling for non-linear characterization of anisotropic materials (Q3018022):

Displaying 22 items.

• Artificial neural network as an incremental nonlinear constitutive model for a finite element code. (Q1420917) (← links)
• A mapping strategy for the identification of structural systems (Q1789474) (← links)
• Multiscale topology optimization using neural network surrogate models (Q1986944) (← links)
• A DNN-based data-driven modeling employing coarse sample data for real-time flexible multibody dynamics simulations (Q2020773) (← links)
• A kernel method for learning constitutive relation in data-driven computational elasticity (Q2024599) (← links)
• Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning (Q2120033) (← links)
• Microstructure-guided deep material network for rapid nonlinear material modeling and uncertainty quantification (Q2160409) (← links)
• Computational mechanics enhanced by deep learning (Q2310108) (← links)
• Nonlinear constitutive models from nanoindentation tests using artificial neural networks (Q2475189) (← links)
• Nonlinear multiscale simulation of elastic beam lattices with anisotropic homogenized constitutive models based on artificial neural networks (Q2667309) (← links)
• Automated discovery of generalized standard material models with EUCLID (Q2683452) (← links)
• Optimization framework for calibration of constitutive models enhanced by neural networks (Q2848328) (← links)
• Computational design of multiaxial tests for anisotropic material characterization (Q3590349) (← links)
• A new neural network-based model for hysteretic behavior of materials (Q3623126) (← links)
• Autoprogressive training of neural network constitutive models (Q4216231) (← links)
• Numerical implementation of a neural network based material model in finite element analysis (Q4462927) (← links)
• (Q5852424) (← links)
• A neural network tool for identifying the material parameters of a finite deformation viscoplasticity model with static recovery (Q5956800) (← links)
• A mechanics‐informed artificial neural network approach in data‐driven constitutive modeling (Q6069980) (← links)
• Hybrid approach to predict the effective properties of heterogeneous materials using artificial neural networks and micromechanical models (Q6089273) (← links)
• An indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method (Q6497200) (← links)
• Deep convolutional neural networks for eigenvalue problems in mechanics (Q6555379) (← links)