World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
61
Citations
11443
World Ranking
2095
National Ranking
664

Overview

I. M. Navon is affiliated with Florida State University in the United States and focuses on research primarily in the field of engineering. The scientist's work spans several subfields, including statistical and nonlinear physics, computational mechanics, atmospheric science, aerospace engineering, and statistics, probability, and uncertainty.

The main research topics covered by I. M. Navon include model reduction and neural networks, lattice Boltzmann simulation studies, probabilistic and robust engineering design, meteorological phenomena and simulations, nuclear engineering thermal-hydraulics, fluid dynamics and vibration analysis, and fluid dynamics and turbulent flows.

The scientist has published extensively across a variety of venues. Frequent publication venues include:

  • International Journal for Numerical Methods in Fluids
  • arXiv (Cornell University)
  • Physics of Fluids
  • Journal of Computational Physics
  • Computer Methods in Applied Mechanics and Engineering

Recent papers by I. M. Navon demonstrate a focus on machine learning methods applied to fluid flows, nonlinear dynamical systems, and geophysical flow data assimilation. Selected recent publications include:

  • Long lead-time daily and monthly streamflow forecasting using machine learning methods, 2020, Journal of Hydrology
  • Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network, 2020, Computer Methods in Applied Mechanics and Engineering
  • Physics-data combined machine learning for parametric reduced-order modelling of nonlinear dynamical systems in small-data regimes, 2022, Computer Methods in Applied Mechanics and Engineering
  • Long short-term memory embedded nudging schemes for nonlinear data assimilation of geophysical flows, 2020, Physics of Fluids
  • A non-linear non-intrusive reduced order model of fluid flow by auto-encoder and self-attention deep learning methods, 2023, International Journal for Numerical Methods in Engineering

Collaborative efforts are a notable aspect of the scientist's career. Frequent co-authors include Charbel Farhat, Cedric Taylor, Nigel Weatherill, Philip Gresho, and David Gartling, each having contributed to multiple joint publications with I. M. Navon.

Best Publications

  • Variational Data Assimilation with an Adiabatic Version of the NMC Spectral Model

    I. M. Navon;X. Zou;J. Derber;J. Sela

  • Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography

    I.M. Navon

  • VARIATM—A FORTRAN program for objective analysis of pseudostress wind fields using large-scale conjugate-gradient minimization

    David M. Legler;I. M. Navon

  • Conjugate-Gradient Methods for Large-Scale Minimization in Meteorology

    I. M. Navon;David M. Legler

  • Long lead-time daily and monthly streamflow forecasting using machine learning methods

    M. Cheng;F. Fang;T. Kinouchi;I.M. Navon

  • Data Assimilation for Numerical Weather Prediction: A Review

    Ionel M. Navon

  • Second-Order Information in Data Assimilation*

    Francois-Xavier Le Dimet;I. M. Navon;Dacian N. Daescu

  • A reduced‐order approach to four‐dimensional variational data assimilation using proper orthogonal decomposition

    Yanhua Cao;Jiang Zhu;I. M. Navon;Zhendong Luo

  • Numerical Experience with Limited-Memory Quasi-Newton and Truncated Newton Methods

    X. Zou;I. M. Navon;M. Berger;K. H. Phua

  • The second order adjoint analysis: Theory and applications

    Zhi Wang;I. M. Navon;F. X. Le Dimet;X. Zou

  • An Optimal Nudging Data Assimilation Scheme Using Parameter Estimation

    X. Zou;I. M. Navon;F. X. Ledimet

  • Non-linear model reduction for the Navier-Stokes equations using residual DEIM method

    D. Xiao;F. Fang;A. G. Buchan;C. C. Pain

  • Non-intrusive reduced order modelling of the Navier-Stokes equations

    Dunhui Xiao;Dunhui Xiao;F. Fang;A.G. Buchan;C.C. Pain

  • Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems

    Dan G. Cacuci;Mihaela Ionescu-Bujor;Ionel Michael Navon

  • POD/DEIM nonlinear model order reduction of an ADI implicit shallow water equations model

    R. ŞTefnescu;I. M. Navon

  • Optimality of variational data assimilation and its relationship with the Kalman filter and smoother

    Zhijin Li;I. M. Navon

  • Reduced-Order Modeling of the Upper Tropical Pacific Ocean Model using Proper Orthogonal Decomposition

    Yanhua Cao;Jiang Zhu;Zhendong Luo;I. M. Navon

  • Objective Analysis of Pseudostress over the Indian Ocean Using a Direct-Minimization Approach

    David M. Legler;I. M. Navon;James J. O'Brien

  • Mixed Finite Element Formulation and Error Estimates Based on Proper Orthogonal Decomposition for the Nonstationary Navier-Stokes Equations

    Zhendong Luo;Jing Chen;I. M. Navon;Xiaozhong Yang

  • Optimal control of cylinder wakes via suction and blowing

    Zhijin Li;I.M. Navon;M.Y. Hussaini;F.-X. Le Dimet

Frequent Co-Authors

Christopher C. Pain
Christopher C. Pain Imperial College London
Xiaolei Zou
Xiaolei Zou Nanjing University of Information Science and Technology
Jichun Li
Jichun Li University of Nevada, Las Vegas
Beny Neta
Beny Neta Naval Postgraduate School
Adrian Sandu
Adrian Sandu Virginia Tech
Peter A. Allison
Peter A. Allison Imperial College London
Zhengyu Liu
Zhengyu Liu The Ohio State University
Ana Isabel Miranda
Ana Isabel Miranda University of Aveiro
Mostafa Abbaszadeh
Mostafa Abbaszadeh Amirkabir University of Technology
Hamid Garmestani
Hamid Garmestani Georgia Institute of Technology

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