Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analy...
Kaydedildi:
| Yazar: | |
|---|---|
| Materyal Türü: | Online |
| Dil: | İngilizce |
| Baskı/Yayın Bilgisi: |
KIT Scientific Publishing
2021
|
| Konular: | |
| Online Erişim: | 34936 |
| Etiketler: |
Etiket eklenmemiş, İlk siz ekleyin!
|
Benzer Materyaller: Framework for Analysis and Identification of Nonlinear Distributed Parameter Systems using Bayesian Uncertainty Quantification based on Generalized Polynomial Chaos
- Surrogate-based uncertainty quantification and parameter optimization in simulations of the West African monsoon
- Model-Based Control of Slab Shapes in Hot Rolling Processes
- Zur probabilistischen Betrachtung von Schienen- und Kraftfahrzeugsystemen unter zufälliger Windanregung
- Nonlinear state and parameter estimation of spatially distributed systems
- Kontinuierliche Selbstkalibrierung von Stereokameras
- Anwendung der Total-Least-Squares-Technik bei geodätischen Problemstellungen