Omar Ghattas

ORCID

Publication Activity (10 Years)

Years Active: 1991-2024
Publications (10 Years): 63

Top Venues

CoRR

SIAM J. Sci. Comput.

J. Comput. Phys.

SIAM/ASA J. Uncertain. Quantification

Publications

Ricardo Baptista, Lianghao Cao, Joshua Chen, Omar Ghattas, Fengyi Li, Youssef M. Marzouk, J. Tinsley Oden
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport. J. Comput. Phys. 503 (2024)
Sreeram Venkat, Milinda Fernando, Stefan Henneking, Omar Ghattas
Fast and Scalable FFT-Based GPU-Accelerated Algorithms for Hessian Actions Arising in Linear Inverse Problems Governed by Autonomous Dynamical Systems. CoRR (2024)
Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas
Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators. CoRR (2024)
Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas
Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning. J. Comput. Phys. 496 (2024)
Nick Alger, Tucker Hartland, Noemi Petra, Omar Ghattas
Point Spread Function Approximation of High-Rank Hessians with Locally Supported Nonnegative Integral Kernels. SIAM J. Sci. Comput. 46 (3) (2024)
Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network. J. Sci. Comput. 95 (1) (2023)
Ki-Tae Kim, Umberto Villa, Matthew D. Parno, Youssef M. Marzouk, Omar Ghattas, Noemi Petra
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty. ACM Trans. Math. Softw. 49 (2) (2023)
Keyi Wu, Peng Chen, Omar Ghattas
A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design. SIAM/ASA J. Uncertain. Quantification 11 (1) (2023)
Lianghao Cao, Keyi Wu, J. Tinsley Oden, Peng Chen, Omar Ghattas
Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data. CoRR (2023)
Keyi Wu, Peng Chen, Omar Ghattas
An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement. SIAM J. Sci. Comput. 45 (1) (2023)
Jeonghun J. Lee, Omar Ghattas
Interior over-penalized enriched Galerkin methods for second order elliptic equations. Comput. Math. Appl. 152 (2023)
Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems. J. Comput. Phys. 486 (2023)
Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas, J. Tinsley Oden
Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm. J. Comput. Phys. 485 (2023)
Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators. CoRR (2023)
Nick Alger, Tucker Hartland, Noemi Petra, Omar Ghattas
Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels. CoRR (2023)
Alex Leviyev, Joshua Chen, Yifei Wang, Omar Ghattas, Aaron Zimmerman
A stochastic Stein Variational Newton method. CoRR (2022)
Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems. CoRR (2022)
Jeonghun J. Lee, Omar Ghattas
Interior over-stabilized enriched Galerkin methods for second order elliptic equations. CoRR (2022)
Milinda Fernando, David Neilsen, Eric W. Hirschmann, Yosef Zlochower, Hari Sundar, Omar Ghattas, George Biros
A GPU-Accelerated AMR Solver for Gravitational Wave Propagation. SC (2022)
Dingcheng Luo, Lianghao Cao, Peng Chen, Omar Ghattas, J. Tinsley Oden
Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm. CoRR (2022)
Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning. CoRR (2022)
Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. CoRR (2022)
Lianghao Cao, Omar Ghattas, J. Tinsley Oden
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. 44 (1) (2022)
Jeonghun J. Lee, Tan Bui-Thanh, Umberto Villa, Omar Ghattas
Forward and inverse modeling of fault transmissibility in subsurface flows. Comput. Math. Appl. 128 (2022)
Keyi Wu, Peng Chen, Omar Ghattas
A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement. CoRR (2021)
Peng Chen, Omar Ghattas
Stein Variational Reduced Basis Bayesian Inversion. SIAM J. Sci. Comput. 43 (2) (2021)
Thomas O'Leary-Roseberry, Xiaosong Du, Anirban Chaudhuri, Joaquim R. R. A. Martins, Karen Willcox, Omar Ghattas
Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data. CoRR (2021)
Karen E. Willcox, Omar Ghattas, Patrick Heimbach
The imperative of physics-based modeling and inverse theory in computational science. Nat. Comput. Sci. 1 (3) (2021)
Umberto Villa, Noemi Petra, Omar Ghattas
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. 47 (2) (2021)
Peng Chen, Omar Ghattas
Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters. SIAM/ASA J. Uncertain. Quantification 9 (4) (2021)
Amal Alghamdi, Marc A. Hesse, Jingyi Chen, Umberto Villa, Omar Ghattas
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data Part II: Quantifying the Uncertainty. CoRR (2021)
Omar Ghattas, Karen Willcox
Learning physics-based models from data: perspectives from inverse problems and model reduction. Acta Numer. 30 (2021)
Ki-Tae Kim, Umberto Villa, Matthew Parno, Youssef Marzouk, Omar Ghattas, Noemi Petra
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty. CoRR (2021)
Peng Chen, Michael R. Haberman, Omar Ghattas
Optimal design of acoustic metamaterial cloaks under uncertainty. J. Comput. Phys. 431 (2021)
Nick Alger, Peng Chen, Omar Ghattas
Tensor train construction from tensor actions, with application to compression of large high order derivative tensors. CoRR (2020)
Thomas O'Leary-Roseberry, Omar Ghattas
Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training. CoRR (2020)
Peng Chen, Omar Ghattas
Projected Stein Variational Gradient Descent. CoRR (2020)
Peng Chen, Omar Ghattas
Stein variational reduced basis Bayesian inversion. CoRR (2020)
Amal Alghamdi, Marc A. Hesse, Jingyi Chen, Omar Ghattas
Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate. CoRR (2020)
Dan Stanzione, John West, R. Todd Evans, Tommy Minyard, Omar Ghattas, Dhabaleswar K. Panda
Frontera: The Evolution of Leadership Computing at the National Science Foundation. PEARC (2020)
Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen, Omar Ghattas
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. CoRR (2020)
Longfei Gao, Omar Ghattas, David E. Keyes
Energy-conserving 3D elastic wave simulation with finite difference discretization on staggered grids with nonconforming interfaces. CoRR (2020)
Nick Alger, Peng Chen, Omar Ghattas
Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors. SIAM J. Sci. Comput. 42 (5) (2020)
Keyi Wu, Peng Chen, Omar Ghattas
A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design. CoRR (2020)
Lianghao Cao, Omar Ghattas, J. Tinsley Oden
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. CoRR (2020)
Peng Chen, Omar Ghattas
Projected Stein Variational Gradient Descent. NeurIPS (2020)
Ilona Ambartsumyan, Wajih Halim Boukaram, Tan Bui-Thanh, Omar Ghattas, David E. Keyes, Georg Stadler, George Turkiyyah, Stefano Zampini
Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs. SIAM J. Sci. Comput. 42 (5) (2020)
Ilona Ambartsumyan, Wajih Bou Karam, Tan Bui-Thanh, Omar Ghattas, David E. Keyes, Georg Stadler, George Turkiyyah, Stefano Zampini
Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs. CoRR (2020)
Thomas O'Leary-Roseberry, Nick Alger, Omar Ghattas
Low Rank Saddle Free Newton: Algorithm and Analysis. CoRR (2020)
Amir Dezfouli, Hassan Ashtiani, Omar Ghattas, Richard Nock, Peter Dayan, Cheng Soon Ong
Disentangled behavioural representations. NeurIPS (2019)
Peng Chen, Umberto Villa, Omar Ghattas
Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty. J. Comput. Phys. 385 (2019)
Umberto Villa, Noemi Petra, Omar Ghattas
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian Inference. CoRR (2019)
Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. NeurIPS (2019)
Nick Alger, Vishwas Rao, Aaron Myers, Tan Bui-Thanh, Omar Ghattas
Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators. SIAM J. Sci. Comput. 41 (4) (2019)
Umberto Villa, Noemi Petra, Omar Ghattas
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems. J. Open Source Softw. 3 (30) (2018)
Kainan Wang, Tan Bui-Thanh, Omar Ghattas
A Randomized Maximum A Posteriori Method for Posterior Sampling of High Dimensional Nonlinear Bayesian Inverse Problems. SIAM J. Sci. Comput. 40 (1) (2018)
Johann Rudi, Georg Stadler, Omar Ghattas
Weighted BFBT Preconditioner for Stokes Flow Problems with Highly Heterogeneous Viscosity. SIAM J. Sci. Comput. 39 (5) (2017)
Alen Alexanderian, Noemi Petra, Georg Stadler, Omar Ghattas
Mean-Variance Risk-Averse Optimal Control of Systems Governed by PDEs with Random Parameter Fields Using Quadratic Approximations. SIAM/ASA J. Uncertain. Quantification 5 (1) (2017)
Nick Alger, Umberto Villa, Tan Bui-Thanh, Omar Ghattas
A Data Scalable Augmented Lagrangian KKT Preconditioner for Large-Scale Inverse Problems. SIAM J. Sci. Comput. 39 (5) (2017)
Ulrich Rüde, Karen Willcox, Lois Curfman McInnes, Hans De Sterck, George Biros, Hans-Joachim Bungartz, James Corones, Evin Cramer, James Crowley, Omar Ghattas, Max D. Gunzburger, Michael Hanke, Robert J. Harrison, Michael A. Heroux, Jan S. Hesthaven, Peter K. Jimack, Chris Johnson, Kirk E. Jordan, David E. Keyes, Rolf H. Krause, Vipin Kumar, Stefan Mayer, Juan Meza, Knut Martin Mørken, J. Tinsley Oden, Linda R. Petzold, Padma Raghavan, Suzanne M. Shontz, Anne E. Trefethen, Peter R. Turner, Vladimir V. Voevodin, Barbara I. Wohlmuth, Carol S. Woodward
Research and Education in Computational Science and Engineering. CoRR (2016)
Johann Rudi, Georg Stadler, Omar Ghattas
Weighted BFBT Preconditioner for Stokes Flow Problems with Highly Heterogeneous Viscosity. CoRR (2016)
Alen Alexanderian, Noemi Petra, Georg Stadler, Omar Ghattas
A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems. SIAM J. Sci. Comput. 38 (1) (2016)
Omar Ghattas, Tobin Isaac, Noémi Petra, Georg Stadler
Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data. VECPAR (2016)
Tobin Isaac, Carsten Burstedde, Lucas C. Wilcox, Omar Ghattas
Recursive Algorithms for Distributed Forests of Octrees. SIAM J. Sci. Comput. 37 (5) (2015)
Tobin Isaac, Noemi Petra, Georg Stadler, Omar Ghattas
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. 296 (2015)
Tobin Isaac, Georg Stadler, Omar Ghattas
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. 37 (6) (2015)
Hari Sundar, Omar Ghattas
A Nested Partitioning Algorithm for Adaptive Meshes on Heterogeneous Clusters. ICS (2015)
Lucas C. Wilcox, Georg Stadler, Tan Bui-Thanh, Omar Ghattas
Discretely Exact Derivatives for Hyperbolic PDE-Constrained Optimization Problems Discretized by the Discontinuous Galerkin Method. J. Sci. Comput. 63 (1) (2015)
Johann Rudi, A. Cristiano I. Malossi, Tobin Isaac, Georg Stadler, Michael Gurnis, Peter W. J. Staar, Yves Ineichen, Costas Bekas, Alessandro Curioni, Omar Ghattas
An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle. SC (2015)
Tobin Isaac, Carsten Burstedde, Lucas C. Wilcox, Omar Ghattas
Recursive Algorithms for Distributed Forests of Octrees. CoRR (2014)
Alen Alexanderian, Noemi Petra, Georg Stadler, Omar Ghattas
-Sparsification. SIAM J. Sci. Comput. 36 (5) (2014)
Tobin Isaac, Georg Stadler, Omar Ghattas
Solution of nonlinear Stokes equations discretized by high-order finite elements on nonconforming and anisotropic meshes, with application to ice sheet dynamics. CoRR (2014)
Tan Bui-Thanh, Omar Ghattas
An Analysis of Infinite Dimensional Bayesian Inverse Shape Acoustic Scattering and Its Numerical Approximation. SIAM/ASA J. Uncertain. Quantification 2 (1) (2014)
Noemi Petra, James Martin, Georg Stadler, Omar Ghattas
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. 36 (4) (2014)
Tan Bui-Thanh, Leszek F. Demkowicz, Omar Ghattas
A Unified Discontinuous Petrov-Galerkin Method and Its Analysis for Friedrichs' Systems. SIAM J. Numer. Anal. 51 (4) (2013)
Tan Bui-Thanh, Omar Ghattas, James Martin, Georg Stadler
A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion. SIAM J. Sci. Comput. 35 (6) (2013)
Jesse Kelly, Omar Ghattas, Hari Sundar
A Nested Partitioning Scheme for Parallel Heterogeneous Clusters. CoRR (2013)
Tan Bui-Thanh, Leszek F. Demkowicz, Omar Ghattas
Constructively well-posed approximation methods with unity inf-sup and continuity constants for partial differential equations. Math. Comput. 82 (284) (2013)
Douglas L. Allaire, George Biros, J. Chambers, Omar Ghattas, D. Kordonowy, Karen Willcox
Dynamic Data Driven Methods for Self-aware Aerospace Vehicles. ICCS (2012)
Tan Bui-Thanh, Carsten Burstedde, Omar Ghattas, James Martin, Georg Stadler, Lucas C. Wilcox
Extreme-scale UQ for Bayesian inverse problems governed by PDEs. SC (2012)
Tan Bui-Thanh, Omar Ghattas
Analysis of an hp-Nonconforming Discontinuous Galerkin Spectral Element Method for Wave Propagation. SIAM J. Numer. Anal. 50 (3) (2012)
Tobin Isaac, Carsten Burstedde, Omar Ghattas
Low-Cost Parallel Algorithms for 2: 1 Octree Balance. IPDPS (2012)
Tan Bui-Thanh, Omar Ghattas, David Higdon
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. 34 (6) (2012)
James Martin, Lucas C. Wilcox, Carsten Burstedde, Omar Ghattas
A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion. SIAM J. Sci. Comput. 34 (3) (2012)
Hari Sundar, George Biros, Carsten Burstedde, Johann Rudi, Omar Ghattas, Georg Stadler
Parallel geometric-algebraic multigrid on unstructured forests of octrees. SC (2012)
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judith Hill, Bart G. van Bloemen Waanders, Omar Ghattas
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations. SIAM J. Sci. Comput. 33 (1) (2011)
Shan Yang, Georg Stadler, Robert D. Moser, Omar Ghattas
A Shape Hessian-Based Boundary Roughness Analysis of Navier-Stokes Flow. SIAM J. Appl. Math. 71 (1) (2011)
Carsten Burstedde, Lucas C. Wilcox, Omar Ghattas
p4est: Scalable Algorithms for Parallel Adaptive Mesh Refinement on Forests of Octrees. SIAM J. Sci. Comput. 33 (3) (2011)
Lucas C. Wilcox, Georg Stadler, Carsten Burstedde, Omar Ghattas
A high-order discontinuous Galerkin method for wave propagation through coupled elastic-acoustic media. J. Comput. Phys. 229 (24) (2010)
Isabel N. Figueiredo, Pedro N. Figueiredo, Georg Stadler, Omar Ghattas, Adérito Araújo
Variational Image Segmentation for Endoscopic Human Colonic Aberrant Crypt Foci. IEEE Trans. Medical Imaging 29 (4) (2010)
Chad Lieberman, Karen Willcox, Omar Ghattas
Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems. SIAM J. Sci. Comput. 32 (5) (2010)
Carsten Burstedde, Omar Ghattas, Michael Gurnis, Tobin Isaac, Georg Stadler, Tim Warburton, Lucas C. Wilcox
Extreme-Scale AMR. SC (2010)
Tan Bui-Thanh, Karen Willcox, Omar Ghattas
Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space. SIAM J. Sci. Comput. 30 (6) (2008)
Carsten Burstedde, Omar Ghattas, Michael Gurnis, Georg Stadler, Eh Tan, Tiankai Tu, Lucas C. Wilcox, Shijie Zhong
Scalable adaptive mantle convection simulation on petascale supercomputers. SC (2008)
Tan Bui-Thanh, Karen Willcox, Omar Ghattas, Bart G. van Bloemen Waanders
Goal-oriented, model-constrained optimization for reduction of large-scale systems. J. Comput. Phys. 224 (2) (2007)
Omar Bashir, Omar Ghattas, Judith Hill, Bart G. van Bloemen Waanders, Karen Willcox
Hessian-Based Model Reduction for Large-Scale Data Assimilation Problems. International Conference on Computational Science (1) (2007)
Tiankai Tu, Hongfeng Yu, Jacobo Bielak, Omar Ghattas, Julio C. López, Kwan-Liu Ma, David R. O'Hallaron, Leonardo Ramírez-Guzmán, Nathan Stone, Ricardo Taborda-Rios, John Urbanic
Analytics challenge - Remote runtime steering of integrated terascale simulation and visualization. SC (2006)
Tiankai Tu, Hongfeng Yu, Leonardo Ramírez-Guzmán, Jacobo Bielak, Omar Ghattas, Kwan-Liu Ma, David R. O'Hallaron
Scalable systems software - From mesh generation to scientific visualization: an end-to-end approach to parallel supercomputing. SC (2006)
Volkan Akcelik, George Biros, Andrei Draganescu, Omar Ghattas, Judith Hill, Bart G. van Bloemen Waanders
Inversion of Airborne Contaminants in a Regional Model. International Conference on Computational Science (3) (2006)
Karen Willcox, Omar Ghattas, Bart G. van Bloemen Waanders, Brett W. Bader
An Optimization Frame work for Goal-Oriented, Model-Based Reduction of Large-Scale Systems. CDC/ECC (2005)

Omar Ghattas.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Omar Ghattas