The analysis of an ill-posed problem using multi-scale resolution and second-order adjoint techniques
By Aleksey K. Alekseev, Ionel M. Navon. 2001
62 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Geophysics, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/629c45a4b0352dd1521e9153dec185d5c85c0463
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- P. A. Vidard
- Julien Favier
- François-Xavier Le Dimet
- Laurent Cordier
- Andrew M. Stuart
- Badr Abou El Majd
- Andrea Piacentini
In Geophysics
- P. A. Vidard
- Julien Favier
- François-Xavier Le Dimet
- Laurent Cordier
- Andrew M. Stuart
- Badr Abou El Majd
- Andrea Piacentini
In Ocean Engineering
- P. A. Vidard
- Julien Favier
- François-Xavier Le Dimet
- Laurent Cordier
- Andrew M. Stuart
- Badr Abou El Majd
- Andrea Piacentini
Citations in the corpus
- Inverse problems: A Bayesian perspective (2010, methodology): “The paper by Alekseev and Navon (2001) uses a wavelet-based approach to study the inverse problem of determining inflow fluid properties from outflow measurements.”
- Calibration of POD reduced‐order models using Tikhonov regularization (2009, methodology): “Incidentally, this approach led us to extend the method of calibration based on Tikhonov regularization [8] recently used in [1] to the first and second-order [9].”
- Determination of optimal nudging coefficients (2003, methodology): “Actually, the regularisation term Jnudg comes from the classical formulation of the cost function of the 4D-Var with imperfect model data assimilation (see Jazwinski, 1970; Tikhonov and Arsenin, 1977; Cohn, Tellus 55A (2003), 1 1997; Alekseev and Navon, 2001).”