Exploring the potential of transfer learning for metamodels of heterogeneous material deformation
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Exploring the potential of transfer learning for metamodels of heterogeneous material deformation
Authors
Keywords
Soft tissue mechanics, Benchmark data, Machine learning
Journal
Journal of the Mechanical Behavior of Biomedical Materials
Volume -, Issue -, Pages 104276
Publisher
Elsevier BV
Online
2020-12-31
DOI
10.1016/j.jmbbm.2020.104276
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Characterization of Spatially-Graded Biomechanical Scaffolds
- (2020) Nicholas R Hugenberg et al. JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME
- Structural Analysis and Optimization of Convolutional Neural Networks with a Small Sample Size
- (2020) Rhett N. D’souza et al. Scientific Reports
- Extraction of mechanical properties of materials through deep learning from instrumented indentation
- (2020) Lu Lu et al. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
- Prediction of composite microstructure stress-strain curves using convolutional neural networks
- (2020) Charles Yang et al. MATERIALS & DESIGN
- Mechanical MNIST: A benchmark dataset for mechanical metamodels
- (2020) Emma Lejeune Extreme Mechanics Letters
- Multimodality Imaging-Based Characterization of Regional Material Properties in a Murine Model of Aortic Dissection
- (2020) Matthew R. Bersi et al. Scientific Reports
- Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
- (2019) Max Schwarzer et al. COMPUTATIONAL MATERIALS SCIENCE
- Adversarial uncertainty quantification in physics-informed neural networks
- (2019) Yibo Yang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Using convolutional neural networks to predict composite properties beyond the elastic limit
- (2019) Charles Yang et al. MRS Communications
- Using machine learning to characterize heart failure across the scales
- (2019) M. Peirlinck et al. Biomechanics and Modeling in Mechanobiology
- Analyzing valve interstitial cell mechanics and geometry with spatial statistics
- (2019) Emma Lejeune et al. JOURNAL OF BIOMECHANICS
- Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible
- (2019) Miguel A. Bessa et al. ADVANCED MATERIALS
- Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression
- (2019) Taeksang Lee et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Multi-fidelity classification using Gaussian processes: Accelerating the prediction of large-scale computational models
- (2019) Francisco Sahli Costabal et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Multi-fidelity optimization of super-cavitating hydrofoils
- (2018) L. Bonfiglio et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- 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
- Propagation of material behavior uncertainty in a nonlinear finite element model of reconstructive surgery
- (2018) Taeksang Lee et al. Biomechanics and Modeling in Mechanobiology
- Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning
- (2018) Paul Z. Hanakata et al. PHYSICAL REVIEW LETTERS
- Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics
- (2018) Gregory H. Teichert et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts
- (2018) Justin S. Tran et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Meta-modeling game for deriving theory-consistent, microstructure-based traction–separation laws via deep reinforcement learning
- (2018) Kun Wang et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A Computational Model of the Biochemomechanics of an Evolving Occlusive Thrombus
- (2017) Manuel K. Rausch et al. JOURNAL OF ELASTICITY
- Printing nature: Unraveling the role of nacre's mineral bridges
- (2017) Grace X. Gu et al. Journal of the Mechanical Behavior of Biomedical Materials
- Self-consistent clustering analysis: An efficient multi-scale scheme for inelastic heterogeneous materials
- (2016) Zeliang Liu et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Tri-layer wrinkling as a mechanism for anchoring center initiation in the developing cerebellum
- (2016) Emma Lejeune et al. Soft Matter
- Surrogate-based methods for black-box optimization
- (2016) Ky Khac Vu et al. International Transactions in Operational Research
- Computational aspects of growth-induced instabilities through eigenvalue analysis
- (2015) A. Javili et al. COMPUTATIONAL MECHANICS
- Heterogeneous growth-induced prestrain in the heart
- (2015) M. Genet et al. JOURNAL OF BIOMECHANICS
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Constrained optimization of an idealized Y-shaped baffle for the Fontan surgery at rest and exercise
- (2010) Weiguang Yang et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Perspectives on biological growth and remodeling
- (2010) D. Ambrosi et al. JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
- A Survey on Transfer Learning
- (2009) Sinno Jialin Pan et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Solution of the nonlinear elasticity imaging inverse problem: the compressible case
- (2008) Nachiket H Gokhale et al. INVERSE PROBLEMS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started