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Title: Data-driven enhancement of fracture paths in random composites

Journal Article · · Mechanics Research Communications

Not Available

Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
NA0003525
OSTI ID:
1682458
Journal Information:
Mechanics Research Communications, Journal Name: Mechanics Research Communications Journal Issue: C Vol. 103; ISSN 0093-6413
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (41)

Implicit constitutive modelling for viscoplasticity using neural networks journal September 1998
Computational homogenization of nonlinear elastic materials using neural networks: NEURAL NETWORKS-BASED COMPUTATIONAL HOMOGENIZATION journal June 2015
Stochastic multiscale modeling of crack propagation in random heterogeneous media
  • Hun, Darith‐Anthony; Guilleminot, Johann; Yvonnet, Julien
  • International Journal for Numerical Methods in Engineering, Vol. 119, Issue 13 https://doi.org/10.1002/nme.6093
journal May 2019
Identification of fracture models based on phase field for crack propagation in heterogeneous lattices in a context of non-separated scales journal September 2018
Parametrically Homogenized Constitutive Models (PHCMs) for Multi-scale Predictions of Fatigue Crack Nucleation in Titanium Alloys journal June 2019
Predicting Microstructure-Sensitive Fatigue-Crack Path in 3D Using a Machine Learning Framework journal July 2019
Stochastic boundary conditions for molecular dynamics simulations of ST2 water journal March 1984
Crack initiation and propagation in materials with randomly distributed holes journal November 1997
A phase field model for rate-independent crack propagation: Robust algorithmic implementation based on operator splits journal November 2010
Identifying the crack path for the phase field approach to fracture with non-maximum suppression journal December 2016
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality journal June 2017
A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning journal June 2018
A phase-field formulation for dynamic cohesive fracture journal May 2019
Predictive modeling of dynamic fracture growth in brittle materials with machine learning journal June 2018
Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications journal February 2019
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks journal May 2019
Projection-based model reduction: Formulations for physics-based machine learning journal January 2019
Convergence of a gradient damage model toward a cohesive zone model journal January 2011
Finite element analysis of V-ribbed belts using neural network based hyperelastic material model journal July 2005
Machine learning strategies for systems with invariance properties journal August 2016
Data-driven probability concentration and sampling on manifold journal September 2016
Machine learning of linear differential equations using Gaussian processes journal November 2017
Hidden physics models: Machine learning of nonlinear partial differential equations journal March 2018
Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification journal August 2018
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification journal December 2018
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations journal February 2019
Entropy-based closure for probabilistic learning on manifolds journal July 2019
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data journal October 2019
Adversarial uncertainty quantification in physics-informed neural networks journal October 2019
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems journal November 2019
Deep learning of vortex-induced vibrations journal December 2018
Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification journal May 2019
A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions journal September 2018
Knowledge‐Based Modeling of Material Behavior with Neural Networks journal January 1991
Stress-Strain Modeling of Sands Using Artificial Neural Networks journal May 1995
Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps journal May 2005
Packing hyperspheres in high-dimensional Euclidean spaces journal October 2006
Microstructure Representation and Reconstruction of Heterogeneous Materials Via Deep Belief Network for Computational Material Design journal May 2017
Polynomial Chaos Expansion of a Multimodal Random Vector journal January 2015
Symplectic Integration of Hamiltonian Systems with Additive Noise journal January 2002
Representing stochastic damage evolution in disordered media as a jump Markov process using the fiber bundle model journal July 2016

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