Cargando…

Predicting stress, strain and deformation fields in materials and structures with graph neural networks

Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have...

Descripción completa

Detalles Bibliográficos
Autores principales: Maurizi, Marco, Gao, Chao, Berto, Filippo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759553/
https://www.ncbi.nlm.nih.gov/pubmed/36528676
http://dx.doi.org/10.1038/s41598-022-26424-3
_version_ 1784852258227224576
author Maurizi, Marco
Gao, Chao
Berto, Filippo
author_facet Maurizi, Marco
Gao, Chao
Berto, Filippo
author_sort Maurizi, Marco
collection PubMed
description Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge. Here, we present an AI-based general framework, implemented through graph neural networks, able to learn complex mechanical behavior of materials from a few hundreds data. Harnessing the natural mesh-to-graph mapping, our deep learning model predicts deformation, stress, and strain fields in various material systems, like fiber and stratified composites, and lattice metamaterials. The model can capture complex nonlinear phenomena, from plasticity to buckling instability, seemingly learning physical relationships between the predicted physical fields. Owing to its flexibility, this graph-based framework aims at connecting materials’ microstructure, base materials’ properties, and boundary conditions to a physical response, opening new avenues towards graph-AI-based surrogate modeling.
format Online
Article
Text
id pubmed-9759553
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-97595532022-12-19 Predicting stress, strain and deformation fields in materials and structures with graph neural networks Maurizi, Marco Gao, Chao Berto, Filippo Sci Rep Article Developing accurate yet fast computational tools to simulate complex physical phenomena is a long-standing problem. Recent advances in machine learning have revolutionized the way simulations are approached, shifting from a purely physics- to AI-based paradigm. Although impressive achievements have been reached, efficiently predicting complex physical phenomena in materials and structures remains a challenge. Here, we present an AI-based general framework, implemented through graph neural networks, able to learn complex mechanical behavior of materials from a few hundreds data. Harnessing the natural mesh-to-graph mapping, our deep learning model predicts deformation, stress, and strain fields in various material systems, like fiber and stratified composites, and lattice metamaterials. The model can capture complex nonlinear phenomena, from plasticity to buckling instability, seemingly learning physical relationships between the predicted physical fields. Owing to its flexibility, this graph-based framework aims at connecting materials’ microstructure, base materials’ properties, and boundary conditions to a physical response, opening new avenues towards graph-AI-based surrogate modeling. Nature Publishing Group UK 2022-12-17 /pmc/articles/PMC9759553/ /pubmed/36528676 http://dx.doi.org/10.1038/s41598-022-26424-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Maurizi, Marco
Gao, Chao
Berto, Filippo
Predicting stress, strain and deformation fields in materials and structures with graph neural networks
title Predicting stress, strain and deformation fields in materials and structures with graph neural networks
title_full Predicting stress, strain and deformation fields in materials and structures with graph neural networks
title_fullStr Predicting stress, strain and deformation fields in materials and structures with graph neural networks
title_full_unstemmed Predicting stress, strain and deformation fields in materials and structures with graph neural networks
title_short Predicting stress, strain and deformation fields in materials and structures with graph neural networks
title_sort predicting stress, strain and deformation fields in materials and structures with graph neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9759553/
https://www.ncbi.nlm.nih.gov/pubmed/36528676
http://dx.doi.org/10.1038/s41598-022-26424-3
work_keys_str_mv AT maurizimarco predictingstressstrainanddeformationfieldsinmaterialsandstructureswithgraphneuralnetworks
AT gaochao predictingstressstrainanddeformationfieldsinmaterialsandstructureswithgraphneuralnetworks
AT bertofilippo predictingstressstrainanddeformationfieldsinmaterialsandstructureswithgraphneuralnetworks