مشروع البحث: Model Reduction in Nonlinear Finite Element Method for Engineering Structures
| dc.contributor.advisor | Prof.. Dr.h.c.Peter Wriggers | |
| dc.date.accessioned | 2026-05-06T07:30:06Z | |
| dc.date.available | 2026-05-06T07:30:06Z | |
| dc.description | ROM is based on eliminating degrees of freedom from the computational problem as appropriate to attain required computational efficiency. In this work, different approaches are introduced to reduce nonlinear models. These approaches are adaptive ROM based on proper orthogonal decomposition combined with BFGS method to decrease the computational cost, adaptive ROM based on the technique called proper snapshots selection, adaptive hyper-ROM based on Missing point estimation, and machine learning approach based on multi support vector regression. | |
| dc.description.abstract | The approximate solutions for complex nonlinear mechanical systems by using standard approaches (finite element, finite volume...etc.) are expensive with respect to both storage and CPU costs. Therefore, Reduced-order model (ROM) is usually thought as computationally inexpensive mathematical representations that offer potential for near real-time analysis such as systems of nonlinear structural mechanics. Nevertheless, | |
| dc.identifier | 959 | |
| dc.identifier.uri | https://dspace.academy.edu.ly/handle/123456789/1958 | |
| dc.subject | Method for Engineering Structures | |
| dc.title | Model Reduction in Nonlinear Finite Element Method for Engineering Structures | |
| dspace.entity.type | Project | |
| project.endDate | 2015 | |
| project.funder.name | هندسة ميكانيكية | |
| project.investigator | محمد أحمد النور النظيف | |
| project.startDate | 2014 |