Deep learning-assisted elastic isotropy identification for architected materials
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Deep learning-assisted elastic isotropy identification for architected materials
Authors
Keywords
Deep learning, Convolutional neural network, Architected material, Elastic isotropy, Rapid mechanical characterization, Transfer learning
Journal
Extreme Mechanics Letters
Volume 43, Issue -, Pages 101173
Publisher
Elsevier BV
Online
2021-01-13
DOI
10.1016/j.eml.2021.101173
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
- Designing complex architectured materials with generative adversarial networks
- (2020) Yunwei Mao et al. Science Advances
- Automated identification and characterization of two-dimensional materials via machine learning-based processing of optical microscope images
- (2020) Juntan Yang et al. Extreme Mechanics Letters
- Energy-Ratio-Based Measure of Elastic Anisotropy
- (2019) Yaopeng Fang et al. PHYSICAL REVIEW LETTERS
- Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses
- (2019) Jie Xiong et al. MRS Communications
- Stiff isotropic lattices beyond the Maxwell criterion
- (2019) Wen Chen et al. Science Advances
- Decoupled effects of bone mass, microarchitecture and tissue property on the mechanical deterioration of osteoporotic bones
- (2019) Pan Liu et al. COMPOSITES PART B-ENGINEERING
- A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys
- (2019) Jie Xiong et al. MATERIALS & DESIGN
- A multiscale XFEM approach to investigate the fracture behavior of bio-inspired composite materials
- (2018) Andre E. Vellwock et al. COMPOSITES PART B-ENGINEERING
- Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
- (2018) Zijiang Yang et al. COMPUTATIONAL MATERIALS SCIENCE
- Three-Dimensional High-Entropy Alloy–Polymer Composite Nanolattices That Overcome the Strength–Recoverability Trade-off
- (2018) Xuan Zhang et al. NANO LETTERS
- Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment
- (2018) Grace X. Gu et al. Materials Horizons
- Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
- (2018) Zijiang Yang et al. ACTA MATERIALIA
- Biomimetic architected materials with improved dynamic performance
- (2018) Zian Jia et al. JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
- Data-driven reduced order models for effective yield strength and partitioning of strain in multiphase materials
- (2017) Marat I. Latypov et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Elastic constants of polycrystals with generally anisotropic crystals
- (2016) Christopher M. Kube et al. JOURNAL OF APPLIED PHYSICS
- Anomalous elastic buckling of layered crystalline materials in the absence of structure slenderness
- (2016) Manrui Ren et al. JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
- Elastic anisotropy of crystals
- (2016) Christopher M. Kube AIP Advances
- Ultralight, ultrastiff mechanical metamaterials
- (2014) X. Zheng et al. SCIENCE
- Mechanical properties of lattice materials via asymptotic homogenization and comparison with alternative homogenization methods
- (2013) Sajad Arabnejad et al. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
- Architectured materials: Expanding materials space
- (2012) Y. Brechet et al. SCRIPTA MATERIALIA
- Universal Elastic Anisotropy Index
- (2008) Shivakumar I. Ranganathan et al. PHYSICAL REVIEW LETTERS
Find the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
SearchCreate your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create Now