Name
Abstract | ||
|---|---|---|
Simulation is often used to estimate the performance of alternative system designs for selecting the best. For a complex system, high-fidelity simulation is usually time-consuming and expensive. In this paper, we provide a new framework that integrates information from the multi-fidelity models to increase efficiency for selecting the best. A Gaussian mixture model is introduced to capture performance clustering information in the multi-fidelity models. Posterior information obtained by a clustering analysis incorporates both cluster-wise information and idiosyncratic information for each design. We propose a new budget allocation method to efficiently allocate high-fidelity simulation replications, utilizing posterior information. Numerical experiments show that the proposed multi-fidelity framework achieves a significant boost in efficiency. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tac.2018.2886165 | IEEE Transactions on Automatic Control |
Keywords | Field | DocType |
Resource management,Computational modeling,Analytical models,Bayes methods,Optimization,Gaussian mixture model | Resource management,Mathematical optimization,Budget allocation,Sampling (statistics),Cluster analysis,Mixture model,Mathematics | Journal |
Volume | Issue | ISSN |
64 | 8 | 0018-9286 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yijie Peng | 1 | 32 | 12.59 |
| Jie Xu | 2 | 81 | 11.71 |
| Loo Hay Lee | 3 | 1159 | 93.96 |
| J. Q. Hu | 4 | 5 | 1.09 |
| Chun-Hung Chen | 5 | 21 | 6.85 |