.png){kind=icon}
Return
Model-data-driven constitutive responses: Application to a multiscale computational framework
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijengsci.2021.103522.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Computational multiscale methods for analyzing and deriving constitutive responses have been used as a tool in engineering problems because of their ability to combine informa-tion at different length scales. However, their application in a nonlinear framework can be limited by high computational costs, numerical difficulties, and/or inaccuracies. In this pa-per, a hybrid methodology is presented which combines classical constitutive laws (model -based), a data-driven correction component, and computational multiscale approaches. A model-based material representation is locally improved with data from lower scales ob-tained by means of a nonlinear numerical homogenization procedure, leading to a model -data-driven approach. Therefore, macroscale simulations explicitly incorporate the true mi-croscale response, maintaining the same level of accuracy that would be obtained with online micro-macro simulations but with a computational cost comparable to classical model-driven approaches. In the proposed approach, both model and data play a funda-mental role allowing for the synergistic integration between a physics-based response and a machine learning black-box. Numerical applications are implemented in two dimensions for different tests investigating both material and structural responses in large deforma-tions. Overall, the presented model-data-driven methodology proves to be more versatile and accurate than methods based on classical model-driven, as well as pure data-driven techniques. In particular, a lower number of training samples is required and robustness is higher than for simulations which solely rely on data. (c) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Model-data-driven
Multiscale simulations
Machine-learning
Computational homogenization
Ordinary kriging
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.7
Papers:
4.9K
Citations:
1.1W
Organization
Cited Papers
Urinary Podocyte Excretion and Proteinuria in Patients Treated with Antivascular Endothelial Growth Factor Therapy for Solid Tumor Malignancies.png)
Oncology
OncologyIF0
PREAI
ShareSaveNumerically explicit potentials for the homogenization of nonlinear elastic heterogeneous materialsOAAI
OAAIShareSaveShareSaveShareSaveShareSaveEnergy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones
PREAI
ShareSave
View more