DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A deep material network approach for predicting the thermomechanical response of composites

Journal Article · · Composites. Part B, Engineering

Not Available

Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA), Office of Defense Nuclear Nonproliferation
OSTI ID:
2267591
Journal Information:
Composites. Part B, Engineering, Journal Name: Composites. Part B, Engineering Journal Issue: C Vol. 272; ISSN 1359-8368
Publisher:
ElsevierCopyright Statement
Country of Publication:
United Kingdom
Language:
English

References (31)

Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy journal July 2022
An FE-DMN method for the multiscale analysis of thermomechanical composites journal February 2022
Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis journal March 2023
Effective thermal expansion coefficients and specific heats of composite materials journal February 1970
Effective properties of composite materials with periodic microstructure: a computational approach journal April 1999
FE2 multiscale approach for modelling the elastoviscoplastic behaviour of long fibre SiC/Ti composite materials journal March 2000
Modeling void growth in polycrystalline materials journal October 2013
Reduced order modeling strategies for computational multiscale fracture journal January 2017
A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials journal March 2019
Deep material network with cohesive layers: Multi-stage training and interfacial failure analysis journal May 2020
Cell division in deep material networks applied to multiscale strain localization modeling journal October 2021
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization journal November 2020
Optimized and autonomous machine learning framework for characterizing pores, particles, grains and grain boundaries in microstructural images journal August 2021
A critical review on the fused deposition modeling of thermoplastic polymer composites journal November 2020
Additive manufacturing of polymeric composites from material processing to structural design journal August 2021
A review of artificial neural networks in the constitutive modeling of composite materials journal November 2021
Micro-mechanics and data-driven based reduced order models for multi-scale analyses of woven composites journal August 2021
Digital-Twin-Enhanced Quality Prediction for the Composite Materials journal March 2023
Homogenization of elastic–viscoplastic heterogeneous materials: Self-consistent and Mori-Tanaka schemes journal June 2009
An elasto-viscoplastic formulation based on fast Fourier transforms for the prediction of micromechanical fields in polycrystalline materials journal May 2012
Rapid inverse calibration of a multiscale model for the viscoplastic and creep behavior of short fiber-reinforced thermoplastics based on Deep Material Networks journal January 2023
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI journal June 2020
Exploring the 3D architectures of deep material network in data-driven multiscale mechanics journal June 2019
On the micromechanics of deep material networks journal September 2020
Deep learning framework for material design space exploration using active transfer learning and data augmentation journal September 2021
Deep material network via a quilting strategy: visualization for explainability and recursive training for improved accuracy journal July 2023
Large Sample Properties of Simulations Using Latin Hypercube Sampling journal May 1987
The Elastic Behaviour of a Crystalline Aggregate journal May 1952
Historical Development of the Newton–Raphson Method journal December 1995
Data-driven Computational Homogenization Using Neural Networks journal December 2020
CRATE: A Python package to perform fast material simulations journal July 2023