Pages that link to "Item:Q649421"

The following pages link to Artificial neural networks in numerical modelling of composites (Q649421):

Displaying 39 items.

• Microstructural characterization of materials by neural network technique (Q430542) (← links)
• A new tool based on artificial neural networks for the design of lightweight ceramic-metal armour against high-velocity impact of solids (Q838549) (← links)
• Neural networks for computing in fracture mechanics. Methods and prospects of applications (Q1309635) (← links)
• Artificial neural network as an incremental nonlinear constitutive model for a finite element code. (Q1420917) (← links)
• Multiscale topology optimization using neural network surrogate models (Q1986944) (← links)
• A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network (Q2020855) (← links)
• Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (Q2021107) (← links)
• Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening (Q2021962) (← links)
• Recurrent neural networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step (Q2072735) (← links)
• On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling (Q2136745) (← links)
• Artificial neural networks in structural dynamics: a new modular radial basis function approach vs. convolutional and feedforward topologies (Q2180502) (← links)
• Micromechanics-based surrogate models for the response of composites: a critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks (Q2190108) (← links)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
• Computational mechanics enhanced by deep learning (Q2310108) (← links)
• A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning (Q2310917) (← links)
• Nonlinear constitutive models from nanoindentation tests using artificial neural networks (Q2475189) (← links)
• Prediction of nonlinear viscoelastic behavior of polymeric composites using an artificial neural network (Q2495896) (← links)
• Artificial neural network application on microstructure-compressive strength relationship of cement mortar (Q2654471) (← links)
• Constrained neural network training and its application to hyperelastic material modeling (Q2667314) (← links)
• Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity (Q2678490) (← links)
• Thermodynamically consistent machine-learned internal state variable approach for data-driven modeling of path-dependent materials (Q2679297) (← links)
• Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate (Q2693414) (← links)
• ANN approach to sorption hysteresis within a coupled hygro-thermo-mechanical FE analysis (Q2709687) (← links)
• Cost optimization of composite floors using neural dynamics model (Q2780775) (← links)
• Prediction of uniaxial compression PFC3D model micro-properties using artificial neural networks (Q2854696) (← links)
• Multiscale approach for the thermomechanical analysis of hierarchical structures (Q2920749) (← links)
• A New Homogenization Formulation for Multifunctional Composites (Q2972077) (← links)
• Artificial neural network modeling of creep behavior in a rotating composite disc (Q3055828) (← links)
• (Q3080303) (← links)
• Generalized self-consistent like method for mechanical degradation of fibrous composites (Q3103942) (← links)
• Material Modeling via Thermodynamics-Based Artificial Neural Networks (Q5021908) (← links)
• Advances in Neural Networks – ISNN 2005 (Q5707481) (← links)
• (Q5852424) (← links)
• Hybrid approach to predict the effective properties of heterogeneous materials using artificial neural networks and micromechanical models (Q6089273) (← links)
• A machine learning-based viscoelastic-viscoplastic model for epoxy nanocomposites with moisture content (Q6096512) (← links)
• Micromechanics-based deep-learning for composites: challenges and future perspectives (Q6540411) (← links)
• Deep convolutional neural networks for eigenvalue problems in mechanics (Q6555379) (← links)
• A thermodynamically consistent physics-informed deep learning material model for short fiber/polymer nanocomposites (Q6557800) (← links)
• A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems (Q6558963) (← links)