Machine learning constitutive models of elastomeric foams

2022 ◽  
Vol 391 ◽  
pp. 114492
Author(s):  
Ari Frankel ◽  
Craig M. Hamel ◽  
Dan Bolintineanu ◽  
Kevin Long ◽  
Sharlotte Kramer
Keyword(s):  
Machine Learning ◽  
Constitutive Models ◽  
Elastomeric Foams

The Machine Learning Embedded Method of Parameters Determination in the Constitutive Models and Potential Applications for Hydrogels

2021 ◽  
Vol 13 (01) ◽  
pp. 2150001 ◽  
Author(s):  
Shoujing Zheng ◽  
Zishun Liu
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Chemical Potential ◽  
Constitutive Models ◽  
Experimental Application ◽  
Potential Applications ◽  
The Neural Networks ◽  
Hydrogel Swelling ◽  
Embedded Method ◽  
Fitting Parameters

We propose a machine learning embedded method of parameters determination in the constitutional models of hydrogels. It is found that the developed logistic regression-like algorithm for hydrogel swelling allows us to determine the fitting parameters based on known swelling ratio and chemical potential. We also put forward the neural networks-like algorithm, which, by its own property, can converge faster as the layer deepens. We then develop neural networks-like algorithm for hydrogel under uniaxial load for experimental application purpose. Finally, we propose several machine learning embedded potential applications for hydrogels, which would provide directions for machine learning-based hydrogel research.

scholarly journals Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter

2021 ◽  
Author(s):  
Duncan Field ◽  
Yanis Ammouche ◽  
José-Maria Peña ◽  
Antoine Jérusalem
Keyword(s):  
Machine Learning ◽  
Composite Materials ◽  
White Matter ◽  
Constitutive Model ◽  
Constitutive Models ◽  
Constitutive Modelling ◽  
Model Parameters ◽  
Brain White Matter ◽  
Specific Loading ◽  
Spatially Varying

AbstractA modular pipeline for improving the constitutive modelling of composite materials is proposed.The method is leveraged here for the development of subject-specific spatially-varying brain white matter mechanical properties. For this application, white matter microstructural information is extracted from diffusion magnetic resonance imaging (dMRI) scans, and used to generate hundreds of representative volume elements (RVEs) with randomly distributed fibre properties. By automatically running finite element analyses on these RVEs, stress-strain curves corresponding to multiple RVE-specific loading cases are produced. A mesoscopic constitutive model homogenising the RVEs’ behaviour is then calibrated for each RVE, producing a library of calibrated parameters against each set of RVE microstructural characteristics. Finally, a machine learning layer is implemented to predict the constitutive model parameters directly from any new microstructure. The results show that the methodology can predict calibrated mesoscopic material properties with high accuracy. More generally, the overall framework allows for the efficient simulation of the spatially-varying mechanical behaviour of composite materials when experimentally measured location-specific fibre geometrical characteristics are provided.

scholarly journals Scientific Machine Learning for Coarse-Grained Constitutive Models

2020 ◽  
Vol 47 ◽  
pp. 693-695
Author(s):  
David González ◽  
Francisco Chinesta ◽  
Elías Cueto
Keyword(s):  
Machine Learning ◽  
Constitutive Models ◽  
Coarse Grained

scholarly journals Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
M. V. Pathan ◽  
S. A. Ponnusami ◽  
J. Pathan ◽  
R. Pitisongsawat ◽  
B. Erice ◽  
...  
Keyword(s):  
Machine Learning ◽  
Constitutive Models ◽  
Principal Component ◽  
Unidirectional Composite ◽  
Transverse Plane ◽  
Supervised Machine Learning ◽  
Physically Based ◽  
Point Correlation ◽  
Point Correlation Function ◽  
Tree Regression Model

Abstract We present an application of data analytics and supervised machine learning to allow accurate predictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in the transverse plane. Predictions are obtained from the analysis of an image of the material microstructure, as well as knowledge of the constitutive models for fibres and matrix, without performing physically-based calculations. The computational framework is based on evaluating the 2-point correlation function of the images of 1800 microstructures, followed by dimensionality reduction via principal component analysis. Finite element (FE) simulations are performed on 1800 corresponding statistical volume elements (SVEs) representing cylindrical fibres in a continuous matrix, loaded in the transverse plane. A supervised machine learning (ML) exercise is performed, employing a gradient-boosted tree regression model with 10-fold cross-validation strategy. The model obtained is able to accurately predict the homogenized properties of arbitrary microstructures.

Constitutive models for predicting field-dependent viscoelastic behavior of magnetorheological elastomer using machine learning

2020 ◽  
Vol 29 (8) ◽  
pp. 087001
Author(s):  
Kasma Diana Saharuddin ◽  
Mohd Hatta Mohammed Ariff ◽  
Irfan Bahiuddin ◽  
Saiful Amri Mazlan ◽  
Siti Aishah Abdul Aziz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Constitutive Models ◽  
Viscoelastic Behavior ◽  
Magnetorheological Elastomer ◽  
Field Dependent

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition
Sign in / Sign up

Export Citation Format

Share Document