Pages that link to "Item:Q1986850"

The following pages link to A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials (Q1986850):

Displaying 50 items.

• A dynamical view of nonlinear conjugate gradient methods with applications to FFT-based computational micromechanics (Q785497) (← links)
• Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
• On the mathematical foundations of the self-consistent clustering analysis for non-linear materials at small strains (Q1988180) (← links)
• Data science for finite strain mechanical science of ductile materials (Q1999552) (← 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)
• Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures (Q2020954) (← links)
• Scale bridging materials physics: active learning workflows and integrable deep neural networks for free energy function representations in alloys (Q2021083) (← links)
• Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity (Q2021107) (← links)
• A GFEM-based reduced-order homogenization model for heterogeneous materials under volumetric and interfacial damage (Q2021955) (← links)
• Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks (Q2033658) (← links)
• Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter (Q2037488) (← links)
• Multiresolution clustering analysis for efficient modeling of hierarchical material systems (Q2039069) (← links)
• A multiscale high-cycle fatigue-damage model for the stiffness degradation of fiber-reinforced materials based on a mixed variational framework (Q2060090) (← links)
• Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks (Q2060125) (← links)
• Exploring the 3D architectures of deep material network in data-driven multiscale mechanics (Q2064793) (← links)
• Interaction-based material network: a general framework for (porous) microstructured materials (Q2072441) (← links)
• A representative volume element network (RVE-net) for accelerating RVE analysis, microscale material identification, and defect characterization (Q2072746) (← links)
• Three-dimensional microstructure generation using generative adversarial neural networks in the context of continuum micromechanics (Q2083129) (← 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)
• Learning constitutive relations using symmetric positive definite neural networks (Q2128348) (← links)
• Inside the black box: a physical basis for the effectiveness of deep generative models of amorphous materials (Q2133569) (← links)
• An FE-DMN method for the multiscale analysis of thermomechanical composites (Q2133887) (← links)
• RotEqNet: rotation-equivariant network for fluid systems with symmetric high-order tensors (Q2138017) (← links)
• Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation (Q2138688) (← links)
• A multiscale, data-driven approach to identifying thermo-mechanically coupled laws -- bottom-up with artificial neural networks (Q2150265) (← links)
• A mixed FFT-Galerkin approach for incompressible or slightly compressible hyperelastic solids under finite deformation (Q2156780) (← links)
• Multiscale modeling of inelastic materials with thermodynamics-based artificial neural networks (TANN) (Q2160403) (← links)
• Microstructure-guided deep material network for rapid nonlinear material modeling and uncertainty quantification (Q2160409) (← links)
• Learning deep implicit Fourier neural operators (IFNOs) with applications to heterogeneous material modeling (Q2160481) (← links)
• Bayesian inference of non-linear multiscale model parameters accelerated by a deep neural network (Q2175257) (← links)
• MAP123: a data-driven approach to use 1D data for 3D nonlinear elastic materials modeling (Q2179201) (← links)
• Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network (Q2179209) (← links)
• A machine learning based plasticity model using proper orthogonal decomposition (Q2184326) (← links)
• An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new time-distributed residual U-net architecture (Q2184471) (← 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)
• Poroelastic model parameter identification using artificial neural networks: on the effects of heterogeneous porosity and solid matrix Poisson ratio (Q2205170) (← links)
• Model-data-driven constitutive responses: application to a multiscale computational framework (Q2234818) (← links)
• Micromechanics-based material networks revisited from the interaction viewpoint; robust and efficient implementation for multi-phase composites (Q2236305) (← links)
• Efficient prediction of the effective nonlinear properties of porous material by FEM-cluster based analysis (FCA) (Q2237326) (← links)
• Cell division in deep material networks applied to multiscale strain localization modeling (Q2237423) (← links)
• An FE-DMN method for the multiscale analysis of short fiber reinforced plastic components (Q2237450) (← links)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
• A machine-learning framework for peridynamic material models with physical constraints (Q2246256) (← links)
• Bayesian neural networks for uncertainty quantification in data-driven materials modeling (Q2246265) (← links)
• Large-deformation reduced order homogenization of polycrystalline materials (Q2246352) (← links)
• \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials (Q2309352) (← links)
• FEA-Net: a physics-guided data-driven model for efficient mechanical response prediction (Q2309378) (← links)
• Deep material network with cohesive layers: multi-stage training and interfacial failure analysis (Q2309395) (← links)