Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems
A greedy algorithm for the construction of a reduced model with reduction in both parameter and state is developed for an efficient solution of statistical inverse problems governed by partial differential equations with distributed parameters. Large-scale models are too costly to evaluate repeatedl...
Main Authors: | Lieberman, Chad E., Willcox, Karen E., Ghattas, O. |
---|---|
Other Authors: | Massachusetts Institute of Technology. Aerospace Controls Laboratory |
Format: | Article |
Language: | en_US |
Published: |
Society of Industrial and Applied Mathematics (SIAM)
2011
|
Online Access: | http://hdl.handle.net/1721.1/60569 https://orcid.org/0000-0003-2156-9338 |
Similar Items
-
Nonlinear Model Reduction for Uncertainty Quantification in Large-Scale Inverse Problems
by: Galbally, David, et al.
Published: (2011) -
Parameter and state model reduction for Bayesian statistical inverse problems
by: Lieberman, Chad Eric
Published: (2010) -
Scalable posterior approximations for large-scale Bayesian inverse problems via likelihood-informed parameter and state reduction
by: Cui, Tiangang, et al.
Published: (2018) -
MODEL REDUCTION FOR LARGE-SCALE SYSTEMS WITH HIGH-DIMENSIONAL PARAMETRIC INPUT SPACE
by: Ghattas, O., et al.
Published: (2010) -
Model Reduction for Large-Scale Systems with High Dimensional Parametric Input Space
by: Bui-Thanh, T., et al.
Published: (2010)