World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
51
Citations
12908
World Ranking
3782
National Ranking
1108

Overview

Habib N. Najm is affiliated with Sandia National Laboratories in the United States. Their research spans multiple fields, primarily concentrating on engineering and materials science. Within these areas, their work extensively covers materials chemistry and incorporates significant investigations in artificial intelligence and aerospace engineering.

The scientist has contributed to key topics including machine learning applications in materials science, advanced combustion engine technologies, neural networks, chemical physics studies, combustion and flame dynamics, as well as nuclear reactor physics and nuclear materials properties.

Frequent publication venues featuring their work include:

  • arXiv (Cornell University)
  • The Journal of Physical Chemistry A
  • Computational Materials Science
  • Combustion Theory and Modelling
  • Combustion and Flame

Recent papers authored or co-authored by Habib N. Najm demonstrate engagement with interdisciplinary research topics. Notable publications include:

  • "2022 Review of Data-Driven Plasma Science," 2023, IEEE Transactions on Plasma Science
  • "The origin of CEMA and its relation to CSP," 2021, Combustion and Flame
  • "Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces," 2023, The Journal of Physical Chemistry A
  • "Sella, an Open-Source Automation-Friendly Molecular Saddle Point Optimizer," 2022, Journal of Chemical Theory and Computation
  • "Geometry optimization speedup through a geodesic approach to internal coordinates," 2021, The Journal of Chemical Physics

Collaborations constitute an important component of their scientific output. Frequent co-authors include Khachik Sargsyan, Judit Zádor, Cosmin Safta, Tiernan Casey, and Pieterjan Robbe.

Best Publications

  • Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics

    Habib N. Najm

  • On the Adequacy of Certain Experimental Observables as Measurements of Flame Burning Rate

    Habib N Najm;Phillip H Paul;Charles J Mueller;Peter S Wyckoff

  • Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes

    Bert J. Debusschere;Habib N. Najm;Philippe P. Pébay;Omar M. Knio

  • Stochastic spectral methods for efficient Bayesian solution of inverse problems

    Youssef M. Marzouk;Habib N. Najm;Larry A. Rahn

  • Uncertainty propagation using Wiener-Haar expansions

    O. P. Le Maître;O. M. Knio;H. N. Najm;R. G. Ghanem

  • A stochastic projection method for fluid flow. I: basic formulation

    Olivier P. Le Maitre;Omar M. Kino;Habib N. Najm;Roger G. Ghanem

  • Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems

    Youssef M. Marzouk;Habib N. Najm

  • A stochastic projection method for fluid flow II.: random process

    Olivier P. Le Maîetre;Matthew T. Reagan;Habib N. Najm;Roger G. Ghanem

  • Multi-resolution analysis of wiener-type uncertainty propagation schemes

    O. P. Le Maître;H. N. Najm;R. G. Ghanem;O. M. Knio

  • Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection

    Matthew T. Reagan;Habib N. Najm;Roger G. Ghanem;Omar M. Knio

  • Planar laser-induced fluorescence imaging of flame heat release rate

    Phillip H. Paul;Habib N. Najm

  • A Semi-implicit Numerical Scheme for Reacting Flow

    Habib N. Najm;Peter S. Wyckoff;Omar M. Knio

  • An automatic procedure for the simplification of chemical kinetic mechanisms based on CSP

    Mauro Valorani;Francesco Creta;Dimitris A. Goussis;Jeremiah C. Lee

  • Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

    Nathan Baker;Frank Alexander;Timo Bremer;Aric Hagberg

  • CSP analysis of a transient flame-vortex interaction: time scales and manifolds

    Mauro Valorani;Habib N. Najm;Dimitris A. Goussis

  • DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING

    Khachik Sargsyan;Cosmin Safta;Habib N. Najm;Bert J. Debusschere

  • Uncertainty quantification in chemical systems

    Habib N. Najm;Bert Debusschere;Youssef Marzouk;S. Widmer

  • Regular Article: A Semi-implicit Numerical Scheme for Reacting Flow

    Omar M Knio;Habib N Najm;Peter S Wyckoff

  • On the Statistical Calibration of Physical Models

    K. Sargsyan;H. N. Najm;R. Ghanem

  • A Study of Flame Observables in Premixed Methane - Air Flames

    H. N. Najm;O. M. Knio;P. H. Paul;P.S. Wyckoff

  • Multi-point pyrometry with real-time surface emissivity compensation

    Mehrdad M. Moslehi;Habib N. Najm

Frequent Co-Authors

Omar M. Knio
Omar M. Knio King Abdullah University of Science and Technology
Dimitris A. Goussis
Dimitris A. Goussis Khalifa University
Roger Ghanem
Roger Ghanem University of Southern California
Michael Frenklach
Michael Frenklach University of California, Berkeley
Joseph C. Oefelein
Joseph C. Oefelein Georgia Institute of Technology
Ali Pinar
Ali Pinar Sandia National Laboratories
Jean-Paul Watson
Jean-Paul Watson Lawrence Livermore National Laboratory

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