|
|
|
|
| LEADER |
01607 am a22002053u 4500 |
| 001 |
96477 |
| 042 |
|
|
|a dc
|
| 100 |
1 |
0 |
|a Liao, Qifeng
|e author
|
| 100 |
1 |
0 |
|a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
|e contributor
|
| 100 |
1 |
0 |
|a Liao, Qifeng
|e contributor
|
| 100 |
1 |
0 |
|a Willcox, Karen E.
|e contributor
|
| 700 |
1 |
0 |
|a Willcox, Karen E.
|e author
|
| 245 |
0 |
0 |
|a A Domain Decomposition Approach for Uncertainty Analysis
|
| 260 |
|
|
|b Society for Industrial and Applied Mathematics,
|c 2015-04-08T20:03:39Z.
|
| 856 |
|
|
|z Get fulltext
|u http://hdl.handle.net/1721.1/96477
|
| 520 |
|
|
|a This paper proposes a decomposition approach for uncertainty analysis of systems governed by partial differential equations (PDEs). The system is split into local components using domain decomposition. Our domain-decomposed uncertainty quantification (DDUQ) approach performs uncertainty analysis independently on each local component in an "offline" phase, and then assembles global uncertainty analysis results using precomputed local information in an "online" phase. At the heart of the DDUQ approach is importance sampling, which weights the precomputed local PDE solutions appropriately so as to satisfy the domain decomposition coupling conditions. To avoid global PDE solves in the online phase, a proper orthogonal decomposition reduced model provides an efficient approximate representation of the coupling functions.
|
| 520 |
|
|
|a United States. Air Force Office of Scientific Research (Grant FA9550-12-1-0420)
|
| 546 |
|
|
|a en_US
|
| 655 |
7 |
|
|a Article
|
| 773 |
|
|
|t SIAM Journal on Scientific Computing
|
