Pages that link to "Item:Q6159331"

The following pages link to Efficient multiscale modeling of heterogeneous materials using deep neural networks (Q6159331):

Displaying 37 items.

• A manifold learning approach for integrated computational materials engineering (Q1639584) (← links)
• CPINet: parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM (Q1982319) (← links)
• A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials (Q1986850) (← links)
• Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
• Smart constitutive laws: inelastic homogenization through machine learning (Q2020776) (← links)
• Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network (Q2020836) (← links)
• Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures (Q2020954) (← links)
• A wavelet-based learning approach assisted multiscale analysis for estimating the effective thermal conductivities of particulate composites (Q2021276) (← links)
• Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter (Q2037488) (← links)
• Exploring the 3D architectures of deep material network in data-driven multiscale mechanics (Q2064793) (← links)
• Deep learning and crystal plasticity: a preconditioning approach for accurate orientation evolution prediction (Q2072494) (← 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)
• Data-driven approach for dynamic homogenization using meta learning (Q2096905) (← links)
• Inside the black box: a physical basis for the effectiveness of deep generative models of amorphous materials (Q2133569) (← links)
• A data-driven approach to full-field nonlinear stress distribution and failure pattern prediction in composites using deep learning (Q2145129) (← links)
• Accelerating phase-field predictions via recurrent neural networks learning the microstructure evolution in latent space (Q2145130) (← 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)
• Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network (Q2179209) (← links)
• A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM (Q2237268) (← links)
• Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
• A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures (Q2237801) (← links)
• Bayesian neural networks for uncertainty quantification in data-driven materials modeling (Q2246265) (← links)
• Deep material network with cohesive layers: multi-stage training and interfacial failure analysis (Q2309395) (← links)
• Transfer learning of deep material network for seamless structure-property predictions (Q2319403) (← links)
• Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems (Q2679283) (← links)
• Computational homogenization of nonlinear elastic materials using neural networks (Q2952866) (← links)
• Computational Homogenization Using Convolutional Neural Networks (Q5051078) (← links)
• A three-dimensional prediction method of stiffness properties of composites based on deep learning (Q6044224) (← links)
• Prediction of numerical homogenization using deep learning for the Richards equation (Q6098948) (← links)
• Solving multi-material problems in solid mechanics using physics-informed neural networks based on domain decomposition technology (Q6099225) (← links)
• Deep learning and multi-level featurization of graph representations of microstructural data (Q6159319) (← links)
• Multiscale Modeling of Metal-Ceramic Spatially Tailored Materials via Gaussian Process Regression and Peridynamics (Q6173029) (← links)
• Micromechanics-based deep-learning for composites: challenges and future perspectives (Q6540411) (← links)
• Solver-free classical computational homogenization for nonlinear periodic heterogeneous media (Q6569918) (← links)
• Unsupervised machine learning classification for accelerating \(\mathrm{FE}^2\) multiscale fracture simulations (Q6641844) (← links)