Omar Ghattas
Orcid: 0000-0001-7742-2509Affiliations:
- University of Texas at Austin, USA
According to our database1,
Omar Ghattas
authored at least 100 papers
between 1991 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
On csauthors.net:
Bibliography
2024
Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning.
J. Comput. Phys., January, 2024
Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators.
CoRR, 2024
2023
Interior over-penalized enriched Galerkin methods for second order elliptic equations.
Comput. Math. Appl., December, 2023
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty.
ACM Trans. Math. Softw., June, 2023
Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network.
J. Sci. Comput., April, 2023
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design.
SIAM/ASA J. Uncertain. Quantification, March, 2023
An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement.
SIAM J. Sci. Comput., February, 2023
Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm.
J. Comput. Phys., 2023
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems.
J. Comput. Phys., 2023
Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels.
CoRR, 2023
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data.
CoRR, 2023
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators.
CoRR, 2023
2022
A Globally Convergent Modified Newton Method for the Direct Minimization of the Ohta-Kawasaki Energy with Application to the Directed Self-Assembly of Diblock Copolymers.
SIAM J. Sci. Comput., 2022
Interior over-stabilized enriched Galerkin methods for second order elliptic equations.
CoRR, 2022
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design.
CoRR, 2022
Comput. Math. Appl., 2022
Proceedings of the SC22: International Conference for High Performance Computing, 2022
2021
Learning physics-based models from data: perspectives from inverse problems and model reduction.
Acta Numer., May, 2021
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs: Part I: Deterministic Inversion and Linearized Bayesian Inference.
ACM Trans. Math. Softw., 2021
The imperative of physics-based modeling and inverse theory in computational science.
Nat. Comput. Sci., 2021
Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters.
SIAM/ASA J. Uncertain. Quantification, 2021
J. Comput. Phys., 2021
CoRR, 2021
A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement.
CoRR, 2021
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data Part II: Quantifying the Uncertainty.
CoRR, 2021
2020
Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs.
SIAM J. Sci. Comput., 2020
Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors.
SIAM J. Sci. Comput., 2020
Energy-conserving 3D elastic wave simulation with finite difference discretization on staggered grids with nonconforming interfaces.
CoRR, 2020
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs.
CoRR, 2020
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design.
CoRR, 2020
Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs.
CoRR, 2020
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate.
CoRR, 2020
CoRR, 2020
Proceedings of the PEARC '20: Practice and Experience in Advanced Research Computing, 2020
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
2019
Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators.
SIAM J. Sci. Comput., 2019
Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty.
J. Comput. Phys., 2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
2018
A Randomized Maximum A Posteriori Method for Posterior Sampling of High Dimensional Nonlinear Bayesian Inverse Problems.
SIAM J. Sci. Comput., 2018
J. Open Source Softw., 2018
2017
Weighted BFBT Preconditioner for Stokes Flow Problems with Highly Heterogeneous Viscosity.
SIAM J. Sci. Comput., 2017
A Data Scalable Augmented Lagrangian KKT Preconditioner for Large-Scale Inverse Problems.
SIAM J. Sci. Comput., 2017
Mean-Variance Risk-Averse Optimal Control of Systems Governed by PDEs with Random Parameter Fields Using Quadratic Approximations.
SIAM/ASA J. Uncertain. Quantification, 2017
2016
A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems.
SIAM J. Sci. Comput., 2016
Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data.
Proceedings of the High Performance Computing for Computational Science - VECPAR 2016, 2016
2015
Solution of Nonlinear Stokes Equations Discretized By High-Order Finite Elements on Nonconforming and Anisotropic Meshes, with Application to Ice Sheet Dynamics.
SIAM J. Sci. Comput., 2015
Discretely Exact Derivatives for Hyperbolic PDE-Constrained Optimization Problems Discretized by the Discontinuous Galerkin Method.
J. Sci. Comput., 2015
Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the Antarctic ice sheet.
J. Comput. Phys., 2015
An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle.
Proceedings of the International Conference for High Performance Computing, 2015
Proceedings of the 29th ACM on International Conference on Supercomputing, 2015
2014
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems.
SIAM J. Sci. Comput., 2014
A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized ℓ<sub>0</sub>-Sparsification.
SIAM J. Sci. Comput., 2014
An Analysis of Infinite Dimensional Bayesian Inverse Shape Acoustic Scattering and Its Numerical Approximation.
SIAM/ASA J. Uncertain. Quantification, 2014
2013
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion.
SIAM J. Sci. Comput., 2013
A Unified Discontinuous Petrov-Galerkin Method and Its Analysis for Friedrichs' Systems.
SIAM J. Numer. Anal., 2013
Constructively well-posed approximation methods with unity inf-sup and continuity constants for partial differential equations.
Math. Comput., 2013
2012
A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion.
SIAM J. Sci. Comput., 2012
Adaptive Hessian-Based Nonstationary Gaussian Process Response Surface Method for Probability Density Approximation with Application to Bayesian Solution of Large-Scale Inverse Problems.
SIAM J. Sci. Comput., 2012
Analysis of an hp-Nonconforming Discontinuous Galerkin Spectral Element Method for Wave Propagation.
SIAM J. Numer. Anal., 2012
Proceedings of the International Conference on Computational Science, 2012
Proceedings of the SC Conference on High Performance Computing Networking, 2012
Proceedings of the SC Conference on High Performance Computing Networking, 2012
Proceedings of the 26th IEEE International Parallel and Distributed Processing Symposium, 2012
2011
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations.
SIAM J. Sci. Comput., 2011
p4est: Scalable Algorithms for Parallel Adaptive Mesh Refinement on Forests of Octrees.
SIAM J. Sci. Comput., 2011
SIAM J. Appl. Math., 2011
2010
IEEE Trans. Medical Imaging, 2010
SIAM J. Sci. Comput., 2010
A high-order discontinuous Galerkin method for wave propagation through coupled elastic-acoustic media.
J. Comput. Phys., 2010
Proceedings of the Conference on High Performance Computing Networking, 2010
2008
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space.
SIAM J. Sci. Comput., 2008
Proceedings of the ACM/IEEE Conference on High Performance Computing, 2008
2007
J. Comput. Phys., 2007
Proceedings of the Computational Science, 2007
2006
Scalable systems software - From mesh generation to scientific visualization: an end-to-end approach to parallel supercomputing.
Proceedings of the ACM/IEEE SC2006 Conference on High Performance Networking and Computing, 2006
Analytics challenge - Remote runtime steering of integrated terascale simulation and visualization.
Proceedings of the ACM/IEEE SC2006 Conference on High Performance Networking and Computing, 2006
Proceedings of the Computational Science, 2006
Proceedings of the Parallel Processing for Scientific Computing, 2006
2005
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.
SIAM J. Sci. Comput., 2005
Parallel Lagrange-Newton-Krylov-Schur Methods for PDE-Constrained Optimization. Part I: The Krylov-Schur Solver.
SIAM J. Sci. Comput., 2005
Proceedings of the ACM/IEEE SC2005 Conference on High Performance Networking and Computing, 2005
Dynamic Data-Driven Inversion For Terascale Simulations: Real-Time Identification Of Airborne Contaminants.
Proceedings of the ACM/IEEE SC2005 Conference on High Performance Networking and Computing, 2005
An Optimization Frame work for Goal-Oriented, Model-Based Reduction of Large-Scale Systems.
Proceedings of the 44th IEEE IEEE Conference on Decision and Control and 8th European Control Conference Control, 2005
2004
Proceedings of the Computational Science, 2004
2003
Proceedings of the ACM/IEEE SC2003 Conference on High Performance Networking and Computing, 2003
Proceedings of the ACM/IEEE SC2003 Conference on High Performance Networking and Computing, 2003
2002
Proceedings of the 2002 ACM/IEEE conference on Supercomputing, 2002
2001
2000
A Parallel Dynamic-Mesh Lagrangian Method for Simulation of Flows with Dynamic Interfaces.
Proceedings of the Proceedings Supercomputing 2000, 2000
1999
Proceedings of the ACM/IEEE Conference on Supercomputing, 1999
1995
Proceedings of the Computer Vision, 1995
1994
Proceedings of the 26th conference on Winter simulation, 1994
1991
Proceedings of the 9th National Conference on Artificial Intelligence, 1991