Efficient Deep Learning for Gradient-Enhanced Stress Dependent Damage Model
- verfasst von
- Xiaoying Zhuang, L. C. Nguyen, Hung Nguyen-Xuan, Naif Alajlan, Timon Rabczuk
- Abstract
This manuscript introduces a computational approach to micro-damage problems using deep learning for the prediction of loading deflection curves. The location of applied forces, dimensions of the specimen and material parameters are used as inputs of the process. The micro-damage is modelled with a gradient-enhanced damage model which ensures the well-posedness of the boundary value and yields mesh-independent results in computational methods such as FEM. We employ the Adam optimizer and Rectified linear unit activation function for training processes and research into the deep neural network architecture. The performance of our approach is demonstrated through some numerical examples including the three-point bending specimen, shear bending on L-shaped specimen and different failure mechanisms.
- Organisationseinheit(en)
-
Institut für Kontinuumsmechanik
- Externe Organisation(en)
-
Ton Duc Thang University
Vietnam National University Ho Chi Minh City
King Saud University
- Typ
- Artikel
- Journal
- Applied Sciences (Switzerland)
- Band
- 10
- ISSN
- 2076-3417
- Publikationsdatum
- 08.04.2020
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Werkstoffwissenschaften (insg.), Instrumentierung, Ingenieurwesen (insg.), Prozesschemie und -technologie, Angewandte Informatik, Fließ- und Transferprozesse von Flüssigkeiten
- Elektronische Version(en)
-
https://doi.org/10.3390/app10072556 (Zugang:
Offen)
