Finite‐Element‐Based Deep‐Learning Model for Deformation Behavior of Digital Materials
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Finite‐Element‐Based Deep‐Learning Model for Deformation Behavior of Digital Materials
Authors
Keywords
-
Journal
Advanced Theory and Simulations
Volume 3, Issue 7, Pages 2000031
Publisher
Wiley
Online
2020-05-18
DOI
10.1002/adts.202000031
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning
- (2020) Chun‐Teh Chen et al. Advanced Science
- Prediction of composite microstructure stress-strain curves using convolutional neural networks
- (2020) Charles Yang et al. MATERIALS & DESIGN
- Machine learning for composite materials
- (2019) Chun-Teh Chen et al. MRS Communications
- Machine-learning Based Design of Active Composite Structures for 4D Printing
- (2019) Craig Hamel et al. Smart Materials and Structures
- Four-dimensional printing of a novel acrylate-based shape memory polymer using digital light processing
- (2019) Hongzhi Wu et al. MATERIALS & DESIGN
- Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
- (2019) F. Ghavamian et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Data-driven multi-scale multi-physics models to derive process–structure–property relationships for additive manufacturing
- (2018) Wentao Yan et al. COMPUTATIONAL MECHANICS
- A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning
- (2018) Kun Wang et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Methods for interpreting and understanding deep neural networks
- (2018) Grégoire Montavon et al. DIGITAL SIGNAL PROCESSING
- A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials
- (2018) Zeliang Liu et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
- (2018) Grace X. Gu et al. Materials Horizons
- A data-driven computational homogenization method based on neural networks for the nonlinear anisotropic electrical response of graphene/polymer nanocomposites
- (2018) Xiaoxin Lu et al. COMPUTATIONAL MECHANICS
- 4D printing with robust thermoplastic polyurethane hydrogel-elastomer trilayers
- (2018) Anna B. Baker et al. MATERIALS & DESIGN
- 4D Printed Actuators with Soft-Robotic Functions
- (2017) María López-Valdeolivas et al. MACROMOLECULAR RAPID COMMUNICATIONS
- Soft material for soft actuators
- (2017) Aslan Miriyev et al. Nature Communications
- A review of 4D printing
- (2017) Farhang Momeni et al. MATERIALS & DESIGN
- Buckling optimization of composite laminates using a hybrid algorithm under Puck failure criterion constraint
- (2016) H Arda Deveci et al. JOURNAL OF REINFORCED PLASTICS AND COMPOSITES
- The status, challenges, and future of additive manufacturing in engineering
- (2015) Wei Gao et al. COMPUTER-AIDED DESIGN
- Sequential Self-Folding Structures by 3D Printed Digital Shape Memory Polymers
- (2015) Yiqi Mao et al. Scientific Reports
- Multiscale methodology for bone remodelling simulation using coupled finite element and neural network computation
- (2010) Ridha Hambli et al. Biomechanics and Modeling in Mechanobiology
- Tunable digital material properties for 3D voxel printers
- (2010) Jonathan Hiller et al. RAPID PROTOTYPING JOURNAL
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started