World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
59
Citations
12177
World Ranking
597
National Ranking
304

Engineering and Technology

D-Index
64
Citations
14115
World Ranking
1664
National Ranking
540

Research.com Recognitions

  • 2015 - ACM Gordon Bell Prize An Extreme-Scale Implicit Solver for Complex PDEs: Highly Heterogeneous Flow in Earth's Mantle
  • 2014 - SIAM Fellow For contributions to optimization of systems governed by partial differential equations and leadership to promote computational science and engineering.

Overview

Omar Ghattas is affiliated with The University of Texas at Austin in the United States. Their research focuses primarily on computer science and engineering, with significant contributions in areas such as artificial intelligence, statistical and nonlinear physics, and computational mechanics.

The scientist has made substantial contributions to several specialized subfields, including:

  • Artificial Intelligence
  • Statistical and Nonlinear Physics
  • Statistics, Probability and Uncertainty
  • Computational Mechanics
  • Materials Chemistry

Ghattas's main research topics cover a range of advanced computational and mathematical disciplines, notably:

  • Model Reduction and Neural Networks
  • Probabilistic and Robust Engineering Design
  • Gaussian Processes and Bayesian Inference
  • Advanced Multi-Objective Optimization Algorithms
  • Seismic Imaging and Inversion Techniques
  • Block Copolymer Self-Assembly
  • Markov Chains and Monte Carlo Methods

The scientist has published extensively in various research venues, with a frequent presence in:

  • arXiv (Cornell University)
  • SIAM Journal on Scientific Computing
  • Computer Methods in Applied Mechanics and Engineering
  • Journal of Computational Physics
  • Computers & Mathematics with Applications

Omar Ghattas has collaborated regularly with several coauthors, including:

  • Thomas O'Leary-Roseberry
  • Peng Chen
  • Umberto Villa
  • Lianghao Cao
  • Karen Willcox

Selected recent publications illustrate the thematic scope and research depth:

  • Frontera: The Evolution of Leadership Computing at the National Science Foundation, 2020, Practice and Experience in Advanced Research Computing
  • Learning physics-based models from data: perspectives from inverse problems and model reduction, 2021, Acta Numerica
  • Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs, 2021, Computer Methods in Applied Mechanics and Engineering
  • Bayesian inference of heterogeneous epidemic models: Application to COVID-19 spread accounting for long-term care facilities, 2021, Computer Methods in Applied Mechanics and Engineering
  • Learning high-dimensional parametric maps via reduced basis adaptive residual networks, 2022, Computer Methods in Applied Mechanics and Engineering

The scientist's research achievements have been recognized through awards including the ACM Gordon Bell Prize in 2015 for work on an extreme-scale implicit solver addressing complex partial differential equations related to Earth's mantle flow.

In 2014, Omar Ghattas was named a SIAM Fellow for contributions to optimization of systems governed by partial differential equations and leadership to promote computational science and engineering.

Best Publications

  • p4est : Scalable Algorithms for Parallel Adaptive Mesh Refinement on Forests of Octrees

    Carsten Burstedde;Lucas C. Wilcox;Omar Ghattas

  • A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion

    James Martin;Lucas C. Wilcox;Carsten Burstedde;Omar Ghattas

  • Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space

    T. Bui-Thanh;K. Willcox;O. Ghattas

  • Large-scale simulation of elastic wave propagation in heterogeneous media on parallel computers

    Hesheng Bao;Jacobo Bielak;Omar Nabih Ghattas;Loukas F Kallivokas

  • A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion

    Tan Bui-Thanh;Omar Ghattas;James Martin;Georg Stadler

  • Parallel Lagrange--Newton--Krylov--Schur Methods for PDE-Constrained Optimization. Part I: The Krylov--Schur Solver

    George Biros;Omar Ghattas

  • The Dynamics of Plate Tectonics and Mantle Flow: From Local to Global Scales

    Georg Stadler;Michael Gurnis;Carsten Burstedde;Lucas C. Wilcox

  • Non-linear model reduction for uncertainty quantification in large-scale inverse problems

    D. Galbally;K. Fidkowski;K. Willcox;Omar Nabih Ghattas

  • A high-order discontinuous Galerkin method for wave propagation through coupled elastic-acoustic media

    Lucas C. Wilcox;Georg Stadler;Carsten Burstedde;Omar Ghattas

  • Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems

    Chad Lieberman;Karen Willcox;Omar Ghattas

  • A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems

    Noemi Petra;James Martin;Georg Stadler;Omar Ghattas

  • A Newton-CG method for large-scale three-dimensional elastic full-waveform seismic inversion

    I. Epanomeritakis;V. Akçelik;Omar Nabih Ghattas;J. Bielak

  • High Resolution Forward And Inverse Earthquake Modeling on Terascale Computers

    Vokan Akcelik;Jacobo Bielak;George Biros;Ioannis Epanomeritakis

  • Large-Scale PDE-Constrained Optimization: An Introduction

    Lorenz T. Biegler;Omar Ghattas;Matthias Heinkenschloss;Bart van Bloemen Waanders

  • From mesh generation to scientific visualization: an end-to-end approach to parallel supercomputing

    Tiankai Tu;Hongfeng Yu;Leonardo Ramirez-Guzman;Jacobo Bielak

  • Goal-oriented, model-constrained optimization for reduction of large-scale systems

    T. Bui-Thanh;K. Willcox;O. Ghattas;B. van Bloemen Waanders

  • Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations

    H. P. Flath;L. C. Wilcox;V. Akçelik;J. Hill

  • Parametric reduced-order models for probabilistic analysis of unsteady aerodynamic applications

    Tan Bui-Thanh;Karen Willcox;Omar Ghattas

  • Large-Scale Inverse Problems and Quantification of Uncertainty

    Lorenz Biegler;George Biros;Omar Nabih Ghattas;Matthias Heinkenschloss

  • Parallel Multiscale Gauss-Newton-Krylov Methods for Inverse Wave Propagation

    Volkan Akcelik;George Biros;Omar Ghattas

  • Parallel Lagrange--Newton--Krylov--Schur Methods for PDE-Constrained Optimization. Part II: The Lagrange--Newton Solver and Its Application to Optimal Control of Steady Viscous Flows

    George Biros;Omar Ghattas

Frequent Co-Authors

Michael Gurnis
Michael Gurnis California Institute of Technology
George Biros
George Biros The University of Texas at Austin
Karen Willcox
Karen Willcox The University of Texas at Austin
David R. O'Hallaron
David R. O'Hallaron Carnegie Mellon University
David E. Keyes
David E. Keyes King Abdullah University of Science and Technology
Jacobo Bielak
Jacobo Bielak Carnegie Mellon University
James F. Antaki
James F. Antaki Cornell University
Shijie Zhong
Shijie Zhong University of Colorado Boulder
Lorenz T. Biegler
Lorenz T. Biegler Carnegie Mellon University

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