相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Micromechanics-based material networks revisited from the interaction viewpoint; robust and efficient implementation for multi-phase composites
Van Dung et al.
EUROPEAN JOURNAL OF MECHANICS A-SOLIDS (2022)
Smart constitutive laws: Inelastic homogenization through machine learning
Hernan J. Logarzo et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2021)
Thermodynamics-based Artificial Neural Networks for constitutive modeling
Filippo Masi et al.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2021)
A Stochastic FE2 Data-Driven Method for Nonlinear Multiscale Modeling
Xiaoxin Lu et al.
MATERIALS (2021)
Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network
Ling Wu et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
A hybrid approach to simulate the homogenized irreversible elastic-plastic deformations and damage of foams by neural networks
Christoph Settgast et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2020)
Using neural networks to represent von Mises plasticity with isotropic hardening
Annan Zhang et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2020)
On the potential of recurrent neural networks for modeling path dependent plasticity
Maysam B. Gorji et al.
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2020)
An intelligent nonlinear meta element for elastoplastic continua: deep learning using a new Time-distributed Residual U-Net architecture
Arnd Koeppe et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
Micromechanics-based surrogate models for the response of composites: A critical comparison between a classical mesoscale constitutive model, hyper-reduction and neural networks
I. B. C. M. Rocha et al.
EUROPEAN JOURNAL OF MECHANICS A-SOLIDS (2020)
A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
Ling Wu et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2020)
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization
Chengping Rao et al.
COMPUTATIONAL MATERIALS SCIENCE (2020)
Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning
Kun Wang et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)
On-the-Fly Adaptivity for Nonlinear Twoscale Simulations Using Artificial Neural Networks and Reduced Order Modeling
Felix Fritzen et al.
FRONTIERS IN MATERIALS (2019)
Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
F. Ghavamian et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)
Deep learning predicts path-dependent plasticity
M. Mozaffar et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
Unified treatment of microscopic boundary conditions and efficient algorithms for estimating tangent operators of the homogenized behavior in the computational homogenization method
Van-Dung Nguyen et al.
COMPUTATIONAL MECHANICS (2017)
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality
M. A. Bessa et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2017)
Multiscale high cycle fatigue models for neat and short fiber reinforced thermoplastic polymers
A. Krairi et al.
INTERNATIONAL JOURNAL OF FATIGUE (2016)
Computational homogenization of nonlinear elastic materials using neural networks
B. A. Le et al.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2015)
Imposing periodic boundary condition on arbitrary meshes by polynomial interpolation
V. -D. Nguyen et al.
COMPUTATIONAL MATERIALS SCIENCE (2012)
Homogenization of the thermoelastic properties of silicon nitride
Johannes Wippler et al.
ACTA MATERIALIA (2011)
Multiscale methodology for bone remodelling simulation using coupled finite element and neural network computation
Ridha Hambli et al.
BIOMECHANICS AND MODELING IN MECHANOBIOLOGY (2011)
Multiaxial fatigue criterion for polypropylene - Automotive applications
A. Berrehili et al.
INTERNATIONAL JOURNAL OF FATIGUE (2010)
Coupling of scales in a multiscale simulation using neural networks
Joerg F. Unger et al.
COMPUTERS & STRUCTURES (2008)
Fatigue life predictions for glassy polymers: A constitutive approach
Roel P. M. Janssen et al.
MACROMOLECULES (2008)
The reduced model multiscale method (R3M) for the non-linear homogenization of hyperelastic media at finite strains
J. Yvonnet et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2007)
Reducing the dimensionality of data with neural networks
G. E. Hinton et al.
SCIENCE (2006)
Characterizing rate-dependent material behaviors in self-learning simulation
Sungmoon Jung et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2006)
Accurate cyclic plastic analysis using a neural network material model
T Furukawa et al.
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS (2004)
Numerical implementation of a neural network based material model in finite element analysis
YMA Hashash et al.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2004)
Artificial neural network as an incremental non-linear constitutive model for a finite element code
M Lefik et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2003)
Artificial neural network for parameter identifications for an elasto-plastic model of superconducting cable under cyclic loading
M Lefik et al.
COMPUTERS & STRUCTURES (2002)
An approach to micro-macro modeling of heterogeneous materials
V Kouznetsova et al.
COMPUTATIONAL MECHANICS (2001)
Simulation of the multi-scale convergence in computational homogenization approaches
K Terada et al.
INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES (2000)
分享论文
