Micromechanics-based deep-learning for composites: Challenges and future perspectives

Mirkhalaf, Mohsen; Rocha, I.B.C.M.

doi:10.1016/j.euromechsol.2024.105242

Micromechanics-based deep-learning for composites

Title

Micromechanics-based deep-learning for composites: Challenges and future perspectives

Author

Mirkhalaf, Mohsen (University of Gothenburg)
Rocha, I.B.C.M. (TU Delft Applied Mechanics)

Date

2024

Abstract

During the last few decades, industries such as aerospace and wind energy (among others) have been remarkably influenced by the introduction of high-performance composites. One challenge, however, for modeling and designing composites is the lack of computational efficiency of accurate high-fidelity models. For design purposes, using conventional optimization approaches typically results in cumbersome procedures due to huge dimensions of the design space and high computational expense of full-field simulations. In recent years, deep learning techniques have been found to be promising methods to increase the efficiency and robustness of a variety of algorithms in multi-scale modeling and design of composites. In this perspective paper, a short overview of the recent developments in micromechanics-based machine learning for composites is given. More importantly, existing challenges for further model enhancements and future perspectives of the field development are elaborated.

Subject

Artificial neural networks
Composite materials
Micromechanics

To reference this document use:

http://resolver.tudelft.nl/uuid:94156d87-7a0a-4af0-a5d0-c83827d67c19

DOI

https://doi.org/10.1016/j.euromechsol.2024.105242

ISSN

0997-7538

Source

European Journal of Mechanics A - Solids, 105

Part of collection

Institutional Repository