Name
Abstract | ||
|---|---|---|
We present an efficient numerical algorithm to approximate the statistical moments of stochastic problems, in the presence of models with different fidelities. The method extends the multi-fidelity approximation method developed in [18], [26]. By combining the efficiency of low-fidelity models and the accuracy of high-fidelity models, our method exhibits fast convergence with a limited number of high-fidelity simulations. We establish an error bound of the method and present several numerical examples to demonstrate the efficiency and applicability of the multi-fidelity algorithm. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jcp.2017.04.022 | Journal of Computational Physics |
Keywords | Field | DocType |
Uncertainty quantification,Stochastic collocation,Multi-fidelity | Convergence (routing),Mathematical optimization,Fidelity,Uncertainty quantification,Collocation method,Mathematics,Method of moments (statistics),Computation | Journal |
Volume | ISSN | Citations |
341 | 0021-9991 | 1 |
PageRank | References | Authors |
0.36 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xueyu Zhu | 1 | 22 | 3.59 |
| Erin M. Linebarger | 2 | 1 | 0.36 |
| Dongbin Xiu | 3 | 1068 | 115.57 |