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This content will become publicly available on April 1, 2023

Title: State Estimation—The Role of Reduced Models
The exploration of complex physical or technological processes usually requires exploiting available information from different sources: (i) physical laws often represented as a family of parameter dependent partial differential equations and (ii) data provided by measurement devices or sensors. The amount of sensors is typically limited and data acquisition may be expensive and in some cases even harmful. This article reviews some recent developments for this “small-data” scenario where inversion is strongly aggravated by the typically large parametric dimension- ality. The proposed concepts may be viewed as exploring alternatives to Bayesian inversion in favor of more deterministic accuracy quantification related to the required computational complexity. We discuss optimality criteria which delineate intrinsic information limits, and highlight the role of reduced models for developing efficient computational strategies. In particular, the need to adapt the reduced models—not to a specific (possibly noisy) data set but rather to the sensor system—is a central theme. This, in turn, is facilitated by exploiting geometric perspectives based on proper stable variational formulations of the continuous model.
Authors:
; ;
Editors:
Rebollo, Tomás C.; Donat, Rosa; Higueras, Inmaculada
Award ID(s):
1720297
Publication Date:
NSF-PAR ID:
10333094
Journal Name:
SEMA SIMAI Springer series
Volume:
1
Page Range or eLocation-ID:
57-77
ISSN:
2199-3041
Sponsoring Org:
National Science Foundation
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