A parameterized‐background data‐weak approach to variational data assimilation: formulation, analysis, and application to acoustics
By Yvon Maday, Anthony T. Patera, J. D. Penn, Masayuki Yano. 2015
153 citations recorded by the source corpus.
Subfields: Mathematical Physics, Statistical and Nonlinear Physics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/833943018ec9d4c41f823db79753c4e42367a2c8
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Mathematical Physics
In Statistical and Nonlinear Physics
- Anthony T. Patera
- Martin A. Grepl
- Gianluigi Rozza
- Karen Veroy
- Yvon Maday
- Andrea Manzoni
- Bernhard Wieland
- Ngoc Cuong Nguyen
In Statistics and Probability
- Anthony T. Patera
- Martin A. Grepl
- Gianluigi Rozza
- Yvon Maday
- Karen Veroy
- Mark Kärcher
- Sébastien Boyaval
- Masayuki Yano
Citations in the corpus
- Reduced basis approximation and a posteriori error bounds for 4D-Var data assimilation (2018, methodology): “We note that the reduced basis method has already been used in a parameterized-background data-weak approach to variational data assimilation in [22,23].”
- The Generalized Empirical Interpolation Method: Stability theory on Hilbert spaces with an application to the Stokes equation (2015, methodology): “In fact, GEIM is a particular instance of this method for the case (with the notations of [19]) N = M and this latter choice is appropriate for situations in which the bias is small.”