Pages that link to "Item:Q4253789"

The following pages link to Implicit constitutive modelling for viscoplasticity using neural networks (Q4253789):

Displaying 50 items.

• Accurate cyclic plastic analysis using a neural network material model (Q597632) (← links)
• Machine learning strategies for systems with invariance properties (Q726815) (← links)
• Learning constitutive relations from indirect observations using deep neural networks (Q781968) (← links)
• Online planning of multiaxial loading path for elastic material identification (Q839270) (← links)
• A cascade optimization methodology for automatic parameter identification and shape/process optimization in metal forming simulation (Q996650) (← links)
• Truncated-Newton training algorithm for neurocomputational viscoplastic model. (Q1418239) (← links)
• Explicit and implicit viscoplastic models for polymeric composite (Q1827637) (← links)
• Determination of constitutive properties from spherical indentation data using neural networks. I: The case of pure kinematic hardening in plasticity laws. II: Plasticity with nonlinear isotropic and kinematic hardening (Q1970633) (← links)
• Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
• Fiber orientation interpolation for the multiscale analysis of short fiber reinforced composite parts (Q1990772) (← links)
• Smart constitutive laws: inelastic homogenization through machine learning (Q2020776) (← links)
• A computational multi-scale model for the stiffness degradation of short-fiber reinforced plastics subjected to fatigue loading (Q2020846) (← links)
• Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (Q2021962) (← links)
• A multiscale high-cycle fatigue-damage model for the stiffness degradation of fiber-reinforced materials based on a mixed variational framework (Q2060090) (← links)
• Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids (Q2060111) (← links)
• Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step (Q2072735) (← links)
• Finite electro-elasticity with physics-augmented neural networks (Q2083132) (← links)
• Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response (Q2096901) (← links)
• Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks (Q2115570) (← links)
• Learning constitutive relations using symmetric positive definite neural networks (Q2128348) (← links)
• Mechanistically informed data-driven modeling of cyclic plasticity via artificial neural networks (Q2138793) (← 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)
• A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths (Q2236174) (← links)
• Micromechanics-based material networks revisited from the interaction viewpoint; robust and efficient implementation for multi-phase composites (Q2236305) (← links)
• Learning nonlocal constitutive models with neural networks (Q2237430) (← links)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
• Recurrent neural networks (RNNs) learn the constitutive law of viscoelasticity (Q2241874) (← links)
• Learning viscoelasticity models from indirect data using deep neural networks (Q2246355) (← links)
• Material behavior modeling with multi-output support vector regression (Q2282402) (← links)
• A surface-to-surface contact search method enhanced by deep learning (Q2308818) (← links)
• \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (Q2309352) (← links)
• Data driven modeling of plastic deformation (Q2309802) (← links)
• Computational mechanics enhanced by deep learning (Q2310108) (← links)
• A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning (Q2310917) (← 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)
• A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation (Q2319404) (← 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)
• Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
• Parameter identification with weightless regularization (Q2761940) (← 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)
• Stochastic identification of elastic constants for anisotropic materials (Q3549848) (← links)
• Multiscale aggregating discontinuities: A method for circumventing loss of material stability (Q3590323) (← links)
• Extracting inelastic metal behaviour through inverse analysis: a shift in focus from material models to material behaviour (Q3612707) (← 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)