TY - JOUR
T1 - Micromechanics-based deep-learning for composites
T2 - Challenges and future perspectives
AU - Mirkhalaf, Mohsen
AU - Rocha, Iuri
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Composite materials
KW - Micromechanics
UR - http://www.scopus.com/inward/record.url?scp=85182901297&partnerID=8YFLogxK
U2 - 10.1016/j.euromechsol.2024.105242
DO - 10.1016/j.euromechsol.2024.105242
M3 - Article
AN - SCOPUS:85182901297
SN - 0997-7538
VL - 105
JO - European Journal of Mechanics, A/Solids
JF - European Journal of Mechanics, A/Solids
M1 - 105242
ER -