Machine learning constitutive models of elastomeric foams

DOI10.1016/j.cma.2021.114492OpenAlexW4205858627WikidataQ115063424 ScholiaQ115063424MaRDI QIDQ2670325

Ari Frankel, Craig M. Hamel, Kevin N. Long, Dan S. Bolintineanu, Sharlotte Kramer

Publication date: 11 March 2022

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2021.114492

zbMATH Keywords

neural networksGaussian processesmachine learningflexible foamshyper elasticity

Mathematics Subject Classification ID

Micromechanics of solids (74M25)

Related Items (7)

Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data ⋮ Geometric learning for computational mechanics. II: Graph embedding for interpretable multiscale plasticity ⋮ Molecular dynamics inferred transfer learning models for finite‐strain hyperelasticity of monoclinic crystals: Sobolev training and validations against physical constraints ⋮ Synergistic integration of deep neural networks and finite element method with applications of nonlinear large deformation biomechanics ⋮ Deep learning framework for multiscale finite element analysis based on data-driven mechanics and data augmentation ⋮ Enhancing phenomenological yield functions with data: challenges and opportunities ⋮ Modular machine learning-based elastoplasticity: generalization in the context of limited data

Uses Software

• GPy
• Keras
• Adam
• GitHub
• PINN

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Machine learning strategies for systems with invariance properties
• Open-cell cellular solids: a constitutive equation for hyperelasticity with deformation-induced anisotropy
• On material representation and constitutive branching in finite compressible elasticity
• The Blatz-Ko material model and homogenization
• Determination of the size of the representative volume element for random composites: Statistical and numerical approach.
• Geometric deep learning for computational mechanics. I: Anisotropic hyperelasticity
• Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
• Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
• Learning constitutive relations using symmetric positive definite neural networks
• A framework for data-driven analysis of materials under uncertainty: countering the curse of dimensionality
• On the stability of Kelvin cell foams under compressive loads
• A 10-node composite tetrahedral finite element for solid mechanics

This page was built for publication: Machine learning constitutive models of elastomeric foams