Dissertation/ Thesis
Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective
| العنوان: | Forward uncertainty quantification with special emphasis on a Bayesian active learning perspective |
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| المؤلفون: | Dang, Chao |
| المساهمون: | Beer, Michael |
| بيانات النشر: | Institutionelles Repositorium der Leibniz Universität Hannover |
| سنة النشر: | 2023 |
| المجموعة: | Institutional Repository of Leibniz Universität Hannover |
| مصطلحات موضوعية: | Uncertainty propagation, Structural reliability analysis, Bayesian active learning, Bayesian inference, Bayesian quadrature, Bayesian optimization, Active learning, Gaussian process, Numerical uncertainty, Line sampling, Uncertainty-Propagation, Strukturzuverlässigkeitsanalyse, Bayesian-active-learning, Bayesian-Inference, Bayesian-Quadrature, Bayesian-Optimization, Active-learning, Gauß-Prozesse, Numerische Unsicherheiten, Line-Sampling, ddc:600 |
| الوصف: | Uncertainty quantification (UQ) in its broadest sense aims at quantitatively studying all sources of uncertainty arising from both computational and real-world applications. Although many subtopics appear in the UQ field, there are typically two major types of UQ problems: forward and inverse uncertainty propagation. The present study focuses on the former, which involves assessing the effects of the input uncertainty in various forms on the output response of a computational model. In total, this thesis reports nine main developments in the context of forward uncertainty propagation, with special emphasis on a Bayesian active learning perspective. The first development is concerned with estimating the extreme value distribution and small first-passage probabilities of uncertain nonlinear structures under stochastic seismic excitations, where a moment-generating function-based mixture distribution approach (MGF-MD) is proposed. As the second development, a triple-engine parallel Bayesian global optimization (T-PBGO) method is presented for interval uncertainty propagation. The third contribution develops a parallel Bayesian quadrature optimization (PBQO) method for estimating the response expectation function, its variable importance and bounds when a computational model is subject to hybrid uncertainties in the form of random variables, parametric probability boxes (p-boxes) and interval models. In the fourth research, of interest is the failure probability function when the inputs of a performance function are characterized by parametric p-boxes. To do so, an active learning augmented probabilistic integration (ALAPI) method is proposed based on offering a partially Bayesian active learning perspective on failure probability estimation, as well as the use of high-dimensional model representation (HDMR) technique. Note that in this work we derive an upper-bound of the posterior variance of the failure probability, which bounds our epistemic uncertainty about the failure probability due to a kind of numerical ... |
| نوع الوثيقة: | doctoral or postdoctoral thesis |
| اللغة: | English |
| Relation: | http://dx.doi.org/10.15488/14746; https://www.repo.uni-hannover.de/handle/123456789/14865 |
| DOI: | 10.15488/14746 |
| الاتاحة: | https://www.repo.uni-hannover.de/handle/123456789/14865 https://doi.org/10.15488/14746 |
| Rights: | Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden. ; frei zugänglich |
| رقم الانضمام: | edsbas.3F294D96 |
| قاعدة البيانات: | BASE |
| DOI: | 10.15488/14746 |
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