Uncertainty Quantification of Reliability Analysis Under Surrogate Model Uncertainty Using Gaussian Process
| Autor: | Chanyoung Park, Sangjune Bae, Nam H. Kim |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
symbols.namesake
020303 mechanical engineering & transports Surrogate model 0203 mechanical engineering Computer science 0211 other engineering and technologies symbols 02 engineering and technology Uncertainty quantification Gaussian process Reliability (statistics) 021106 design practice & management Reliability engineering |
| Zdroj: | Volume 2B: 44th Design Automation Conference. |
| DOI: | 10.1115/detc2018-85541 |
| Popis: | The main objective of this paper is to quantify the effect of surrogate model uncertainty on reliability in addition to the aleatory randomness of the input variables, especially when Kriging surrogate model is utilized where the prediction uncertainty is modeled with a normal distribution. A novel approach is presented which requires only a single set of Monte Carlo Simulation (MCS) to precisely estimate the variance of reliability that is used as an uncertainty measure. It is found that the method only requires the bivariate cumulative distribution function, and the result shows that the uncertainty is well quantified without going through multiple numbers of MCS. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|