68 citations · 89 across the 10 of their papers we have counts for
12 papers
Deflation-Based Spectral Preconditioning for the Conjugate Gradient Method on Perturbed Systems
Youssef Diouane, Selime Gürol, Oussama Mouhtal +1
We consider sequences of large symmetric positive-definite linear systems in which spectral information from a reference matrix is reused to accelerate the solution of a nearby…
Accelerating an ensemble of variational data assimilations with randomized preconditioning
I. Daužickaitė, S. Gürol, M. Destouches +2
Ensembles of variational data assimilations (EDA) require solving systems of linear equations with iterative methods. The solution process can be accelerated using a limited memory…
A Spectral Preconditioner for the Conjugate Gradient Method with Iteration Budget
Youssef Diouane, Selime Gürol, Oussama Mouhtal +1
We study the solution of large symmetric positive-definite linear systems in a matrix-free setting with a limited iteration budget. We focus on the preconditioned conjugate gradien…
An Introduction to Solving the Least-Squares Problem in Variational Data Assimilation
I. Daužickaitė, M. A. Freitag, S. Gürol +4
Variational data assimilation is a technique for combining measured data with dynamical models. It is a key component of Earth system state estimation and is commonly used in weath…
Block Alpha-Circulant Preconditioners for All-at-Once Diffusion-Based Covariance Operators
Jemima M. Tabeart, Selime Gürol, John W. Pearson +1
Covariance matrices are central to data assimilation and inverse methods derived from statistical estimation theory. Previous work has considered the application of an all-at-once…
An Efficient Scaled spectral preconditioner for sequences of symmetric positive definite linear systems
Youssef Diouane, Selime Gürol, Oussama Mouhtal +1
We explore a scaled spectral preconditioner for the efficient solution of sequences of symmetric and positive-definite linear systems. We design the scaled preconditioner not only…
A filtered multilevel Monte Carlo method for estimating the expectation of cell-centered discretized random fields
Jérémy Briant, Paul Mycek, Mayeul Destouches +5
In this paper, we investigate the use of multilevel Monte Carlo (MLMC) methods for estimating the expectation of discretized random fields. Specifically, we consider a setting in w…
A general error analysis for randomized low-rank approximation methods
Youssef Diouane, Selime Gürol, Alexandre Scotto Di Perrotolo +1
We propose a general error analysis related to the low-rank approximation of a given real matrix in both the spectral and Frobenius norms. First, we derive deterministic error boun…
Latent Space Data Assimilation by using Deep Learning
Mathis Peyron, Anthony Fillion, Selime Gürol +4
Performing Data Assimilation (DA) at a low cost is of prime concern in Earth system modeling, particularly at the time of big data where huge quantities of observations are availab…
Data Assimilation Networks
Pierre Boudier, Anthony Fillion, Serge Gratton +2
Data assimilation (DA) aims at forecasting the state of a dynamical system by combining a mathematical representation of the system with noisy observations taking into account thei…
A note on preconditioning weighted linear least squares, with consequences for weakly-constrained variational data assimilation
Serge Gratton, Selime Gürol, Ehouarn Simon +1
The effect of preconditioning linear weighted least-squares using an approximation of the model matrix is analyzed, showing the interplay of the eigenstructures of both the model a…
On the use of the saddle formulation in weakly-constrained 4D-VAR data assimilation
S. Gratton, S. Gürol, E. Simon +1
This paper discusses the practical use of the saddle variational formulation for the weakly-constrained 4D-VAR method in data assimilation. It is shown that the method, in its orig…