Convolutional neural network for fast prediction of the effective properties of domains with random inclusions

We consider a heterogeneous domain with random inclusions in two-dimensional and three-dimensional formulations. For generation of the train and test datasets, we numerically calculate the effective properties for a given geometry of the heterogeneous domain. We construct a machine learning method t...

Full description

Saved in:
Bibliographic Details
Published in:Journal of physics. Conference series 2019-02, Vol.1158 (4), p.42034
Main Authors: Vasilyeva, M, Tyrylgin, A
Format: Article
Language:English
Subjects:
Artificial neural networks
Domains
Inclusions
Machine learning
Numerical prediction
Three dimensional models
Two dimensional models
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We consider a heterogeneous domain with random inclusions in two-dimensional and three-dimensional formulations. For generation of the train and test datasets, we numerically calculate the effective properties for a given geometry of the heterogeneous domain. We construct a machine learning method to learn a map between local heterogeneous geometries and effective properties. We present numerical results for prediction of the effective properties for 2D and 3D model problems.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1158/4/042034