Ionel M. Navon
Computational Mechanics · 2001–2017
A researcher in the Science corpus: 13 papers; trusts 61 researchers and is trusted by 34 researchers here, as inferred from the citation graph.
Subfields
- Computational Mechanics — 39 citations into it; #1,408 of 10,001 by trust here, #2,715 by citations
- Statistical and Nonlinear Physics — 39 citations into it; #3,319 of 10,127 by trust here, #4,439 by citations
- Global and Planetary Change — 30 citations into it; #1,231 of 10,225 by trust here, #3,466 by citations
- Atmospheric Science — 29 citations into it; #264 of 4,804 by trust here, #899 by citations
- Optimization & Training — 21 citations into it
- Statistics and Probability — 19 citations into it
- Numerical Analysis — 17 citations into it; #2,053 of 3,586 by trust here, #1,968 by citations
- Computational Theory and Mathematics — 14 citations into it
Whom Ionel M. Navon trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Eugenia Kalnay
- Jorge Nocedal
- M. Powell
- Dimitri P. Bertsekas
- Albert Tarantola
- Andrew F. Bennett
- Dong C. Liu
- G. Desroziers
- O. Talagrand
- R. Langland
Who trusts Ionel M. Navon overall
- Dacian N. Daescu
- Juan J. Ruiz
- P. A. Vidard
- Andrew M. Stuart
- Toni Lassila
- Dunhui Xiao
- Wei Bai
- Manuel Pulido
- Li Li
- F. Enet
Papers from Ionel M. Navon's perspective
Ordered by how strongly the researchers Ionel M. Navon trusts endorse each paper. Same corpus, a different order for every researcher.
- Atmospheric Modeling, Data Assimilation and Predictability
- An Ensemble Adjustment Kalman Filter for Data Assimilation
- Numerical Optimization (Springer Series in Operations Research and Financial Engineering)
- Diagnosis and adaptive tuning of observation‐error parameters in a variational assimilation
- The Ensemble Kalman Filter: theoretical formulation and practical implementation
- Updating Quasi-Newton Matrices With Limited Storage
- Observing‐system experiments in the ECMWF 4D‐Var data assimilation system
- Diagnosis and tuning of observational error in a quasi‐operational data assimilation setting
- Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory
- Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition
Their papers
- Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems (2005) — 135 citations
- Non-linear Petrov-Galerkin methods for reduced order modelling of the Navier-Stokes equations using a mixed finite element pair (2013) — 92 citations
- A parameterized non-intrusive reduced order model and error analysis for general time-dependent nonlinear partial differential equations and its applications (2017) — 126 citations
- Non-intrusive reduced order modelling of the Navier-Stokes equations (2015) — 157 citations
- Data Assimilation for Numerical Weather Prediction: A Review (2009) — 221 citations
- A POD reduced order unstructured mesh ocean modelling method for moderate Reynolds number flows (2009) — 86 citations
- A Dual-Weighted Approach to Order Reduction in 4DVAR Data Assimilation (2008) — 96 citations
- A reduced‐order approach to four‐dimensional variational data assimilation using proper orthogonal decomposition (2007) — 207 citations
- Efficiency of a POD‐based reduced second‐order adjoint model in 4D‐Var data assimilation (2007) — 94 citations
- A comparative study of the performance of high resolution advection schemes in the context of data assimilation (2006) — 299 citations
- Second-Order Information in Data Assimilation* (2002) — 201 citations
- The analysis of an ill-posed problem using multi-scale resolution and second-order adjoint techniques (2001) — 62 citations
- Optimality of variational data assimilation and its relationship with the Kalman filter and smoother (2001) — 128 citations