Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion
Name
Lieberman-2012-GOAL-ORIENTED INFERENCE.pdf
Size
3.12 MB
Format
Adobe PDF
Checksum (MD5)
a880cc876653dd26fa8de640a192d0b0
Author(s)
Lieberman, Chad E.
Willcox, Karen E.
Date Issued
July 2012
Journal
SIAM Journal on Scientific Computing
Publisher
Society for Industrial and Applied Mathematics
Citation
Lieberman, Chad, and Karen Willcox. “Goal-Oriented Inference: Approach, Linear Theory, and Application to Advection Diffusion.” SIAM Journal on Scientific Computing 34.4 (2012): A1880–A1904. CrossRef. Web. © 2012, Society for Industrial and Applied Mathematics.
Version
Final published version
Abstract
Inference of model parameters is one step in an engineering process often ending in predictions that support decision in the form of design or control. Incorporation of end goals into the inference process leads to more efficient goal-oriented algorithms that automatically target the most relevant parameters for prediction. In the linear setting the control-theoretic concepts underlying balanced truncation model reduction can be exploited in inference through a dimensionally optimal subspace regularizer. The inference-for-prediction method exactly replicates the prediction results of either truncated singular value decomposition, Tikhonov-regularized, or Gaussian statistical inverse problem formulations independent of data; it sacrifices accuracy in parameter estimate for online efficiency. The new method leads to low-dimensional parameterization of the inverse problem enabling solution on smartphones or laptops in the field.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1137/110857763
