Scalable gradient-based approaches for topology and shape optimization under uncertainty
Citation Link: https://doi.org/10.15480/882.17010
Publikationstyp
Doctoral Thesis
Date Issued
2026
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2026-04-17
Institute
TORE-DOI
Citation
Technische Universität Hamburg (2026)
This dissertation addresses the challenge of finding scalable frameworks for structural optimization under uncertainty. Therefore, efficient and non-intrusive formulations for the gradient computation of different Taylor series-based probabilistic methods are developed and a new probabilistic approach is presented. Additionally, challenges encountered by the multigrid finite element solver when dealing with (uncertain) anisotropic materials are discussed and a solution is presented.
Subjects
structural optimization
uncertainty quantification
robust design optimization
perturbation method
first-order second-moment method
topology optimization
DDC Class
620: Engineering
624.17: Structural Analysis and Design
519: Applied Mathematics, Probabilities