Geometric Reasoning for Shape Design. - George Turkiyyah; Omar Ghattas (AAAI, 1991)

Finite element pre-operative simulation of cementless hip replacement. - Christopher D. Visnic; Robert H. Reid; Omar Ghattas; Anthony M. DiGioia; Branislav Jaramaz (WSC, 1994)

Towards More Capable and Less Invasive Robotic Surgery in Orthopaedics. - R. V. O'Toole III; David A. Simon; Branislav Jaramaz; Omar Ghattas; Mike Blackwell; Loukas F. Kallivokas; F. Morgan; Christopher D. Visnic; Anthony M. DiGioia; Takeo Kanade (CVRMed, 1995)

Parallel Netwon-Krylov Methods for PDE-Constrained Optimization. - George Biros; Omar Ghattas (SC, 1999)

A Parallel Dynamic-Mesh Lagrangian Method for Simulation of Flows with Dynamic Interfaces. - James F. Antaki; Guy E. Blelloch; Omar Ghattas; Ivan Malcevic; Gary L. Miller; Noel Walkington (SC, 2000)

Optimal Design of Truss Structures by Logic-Based Branch and Cut. - Srinivas Bollapragada; Omar Ghattas; John N. Hooker (Oper. Res., 2001)

Parallel multiscale Gauss-Newton-Krylov methods for inverse wave propagation. - Volkan Akcelik; George Biros; Omar Ghattas (SC, 2002)

High Resolution Forward And Inverse Earthquake Modeling on Terascale Computers. - Volkan Akcelik; Jacobo Bielak; George Biros; Ioannis Epanomeritakis; Antonio Fernandez; Omar Ghattas; Eui Joong Kim; Julio C. López 001; David R. O'Hallaron; Tiankai Tu; John Urbanic (SC, 2003)

Visualizing Very Large-Scale Earthquake Simulations. - Kwan-Liu Ma; Aleksander Stompel; Jacobo Bielak; Omar Ghattas; Eui Joong Kim (SC, 2003)

A Framework for Online Inversion-Based 3D Site Characterization. - Volkan Akcelik; Jacobo Bielak; George Biros; Ioannis Epanomeritakis; Omar Ghattas; Loukas F. Kallivokas; Eui Joong Kim (International Conference on Computational Science, 2004)

An Optimization Frame work for Goal-Oriented, Model-Based Reduction of Large-Scale Systems. - Karen Willcox; Omar Ghattas; Bart G. van Bloemen Waanders; Brett W. Bader (CDC/ECC, 2005)

Dynamic Data-Driven Inversion For Terascale Simulations: Real-Time Identification Of Airborne Contaminants. - Volkan Akcelik; George Biros; Andrei Draganescu; Judith Hill; Omar Ghattas; Bart G. van Bloemen Waanders (SC, 2005)

Scalable Parallel Octree Meshing for TeraScale Applications. - Tiankai Tu; David R. O'Hallaron; Omar Ghattas (SC, 2005)

Parallel Lagrange-Newton-Krylov-Schur Methods for PDE-Constrained Optimization. Part I: The Krylov-Schur Solver. - George Biros; Omar Ghattas (SIAM J. Sci. Comput., 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. - George Biros; Omar Ghattas (SIAM J. Sci. Comput., 2005)

Parallel Algorithms for PDE-Constrained Optimization. - Volkan Akçelik; George Biros; Omar Ghattas; Judith Hill; David E. Keyes; Bart G. van Bloemen Waanders (Parallel Processing for Scientific Computing, 2006)

Inversion of Airborne Contaminants in a Regional Model. - Volkan Akcelik; George Biros; Andrei Draganescu; Omar Ghattas; Judith Hill; Bart G. van Bloemen Waanders (International Conference on Computational Science (3), 2006)

Analytics challenge - Remote runtime steering of integrated terascale simulation and visualization. - Tiankai Tu; Hongfeng Yu 001; Jacobo Bielak; Omar Ghattas; Julio C. López 001; Kwan-Liu Ma; David R. O'Hallaron; Leonardo Ramírez-Guzmán; Nathan Stone; Ricardo Taborda-Rios; John Urbanic (SC, 2006)

Scalable systems software - From mesh generation to scientific visualization: an end-to-end approach to parallel supercomputing. - Tiankai Tu; Hongfeng Yu 001; Leonardo Ramírez-Guzmán; Jacobo Bielak; Omar Ghattas; Kwan-Liu Ma; David R. O'Hallaron (SC, 2006)

Hessian-Based Model Reduction for Large-Scale Data Assimilation Problems. - Omar Bashir; Omar Ghattas; Judith Hill; Bart G. van Bloemen Waanders; Karen Willcox (International Conference on Computational Science (1), 2007)

Goal-oriented, model-constrained optimization for reduction of large-scale systems. - Tan Bui-Thanh; Karen Willcox; Omar Ghattas; Bart G. van Bloemen Waanders (J. Comput. Phys., 2007)

Scalable adaptive mantle convection simulation on petascale supercomputers. - Carsten Burstedde; Omar Ghattas; Michael Gurnis; Georg Stadler; Eh Tan; Tiankai Tu; Lucas C. Wilcox; Shijie Zhong (SC, 2008)

Model Reduction for Large-Scale Systems with High-Dimensional Parametric Input Space. - Tan Bui-Thanh; Karen Willcox; Omar Ghattas (SIAM J. Sci. Comput., 2008)

Extreme-Scale AMR. - Carsten Burstedde; Omar Ghattas; Michael Gurnis; Tobin Isaac; Georg Stadler; Tim Warburton; Lucas C. Wilcox (SC, 2010)

A high-order discontinuous Galerkin method for wave propagation through coupled elastic-acoustic media. - Lucas C. Wilcox; Georg Stadler; Carsten Burstedde; Omar Ghattas (J. Comput. Phys., 2010)

Parameter and State Model Reduction for Large-Scale Statistical Inverse Problems. - Chad Lieberman; Karen Willcox; Omar Ghattas (SIAM J. Sci. Comput., 2010)

Variational Image Segmentation for Endoscopic Human Colonic Aberrant Crypt Foci. - Isabel N. Figueiredo; Pedro N. Figueiredo; Georg Stadler; Omar Ghattas; Adérito Araújo (IEEE Trans. Medical Imaging, 2010)

A Shape Hessian-Based Boundary Roughness Analysis of Navier-Stokes Flow. - Shan Yang; Georg Stadler; Robert D. Moser; Omar Ghattas (SIAM J. Appl. Math., 2011)

p4est: Scalable Algorithms for Parallel Adaptive Mesh Refinement on Forests of Octrees. - Carsten Burstedde; Lucas C. Wilcox; Omar Ghattas (SIAM J. Sci. Comput., 2011)

Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations. - H. P. Flath; Lucas C. Wilcox; Volkan Akcelik; Judith Hill; Bart G. van Bloemen Waanders; Omar Ghattas (SIAM J. Sci. Comput., 2011)

Low-Cost Parallel Algorithms for 2: 1 Octree Balance. - Tobin Isaac; Carsten Burstedde; Omar Ghattas (IPDPS, 2012)

Extreme-scale UQ for Bayesian inverse problems governed by PDEs. - Tan Bui-Thanh; Carsten Burstedde; Omar Ghattas; James Martin; Georg Stadler; Lucas C. Wilcox (SC, 2012)

Parallel geometric-algebraic multigrid on unstructured forests of octrees. - Hari Sundar; George Biros; Carsten Burstedde; Johann Rudi; Omar Ghattas; Georg Stadler (SC, 2012)

Dynamic Data Driven Methods for Self-aware Aerospace Vehicles. - Douglas L. Allaire; George Biros; J. Chambers; Omar Ghattas; D. Kordonowy; Karen Willcox (ICCS, 2012)

Analysis of an hp-Nonconforming Discontinuous Galerkin Spectral Element Method for Wave Propagation. - Tan Bui-Thanh; Omar Ghattas (SIAM J. Numer. Anal., 2012)

Adaptive Hessian-Based Nonstationary Gaussian Process Response Surface Method for Probability Density Approximation with Application to Bayesian Solution of Large-Scale Inverse Problems. - Tan Bui-Thanh; Omar Ghattas; David Higdon (SIAM J. Sci. Comput., 2012)

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 (SIAM J. Sci. Comput., 2012)

A Nested Partitioning Scheme for Parallel Heterogeneous Clusters. - Jesse Kelly; Omar Ghattas; Hari Sundar (CoRR, 2013)

Constructively well-posed approximation methods with unity inf-sup and continuity constants for partial differential equations. - Tan Bui-Thanh; Leszek F. Demkowicz; Omar Ghattas (Math. Comput., 2013)

A Unified Discontinuous Petrov-Galerkin Method and Its Analysis for Friedrichs' Systems. - Tan Bui-Thanh; Leszek F. Demkowicz; Omar Ghattas (SIAM J. Numer. Anal., 2013)

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 (SIAM J. Sci. Comput., 2013)

An Analysis of Infinite Dimensional Bayesian Inverse Shape Acoustic Scattering and Its Numerical Approximation. - Tan Bui-Thanh; Omar Ghattas (SIAM/ASA J. Uncertain. Quantification, 2014)

A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized ℓ0-Sparsification. - Alen Alexanderian; Noemi Petra; Georg Stadler; Omar Ghattas (SIAM J. Sci. Comput., 2014)

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 (SIAM J. Sci. Comput., 2014)

A Nested Partitioning Algorithm for Adaptive Meshes on Heterogeneous Clusters. - Hari Sundar; Omar Ghattas (ICS, 2015)

An extreme-scale implicit solver for complex PDEs: highly heterogeneous flow in earth's mantle. - Johann Rudi; A. Cristiano I. Malossi; Tobin Isaac; Georg Stadler; Michael Gurnis; Peter W. J. Staar; Yves Ineichen; Costas Bekas; Alessandro Curioni; Omar Ghattas (SC, 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. - Tobin Isaac; Noemi Petra; Georg Stadler; Omar Ghattas (J. Comput. Phys., 2015)

Discretely Exact Derivatives for Hyperbolic PDE-Constrained Optimization Problems Discretized by the Discontinuous Galerkin Method. - Lucas C. Wilcox; Georg Stadler; Tan Bui-Thanh; Omar Ghattas (J. Sci. Comput., 2015)

Recursive Algorithms for Distributed Forests of Octrees. - Tobin Isaac; Carsten Burstedde; Lucas C. Wilcox; Omar Ghattas (SIAM J. Sci. Comput., 2015)

Solution of Nonlinear Stokes Equations Discretized By High-Order Finite Elements on Nonconforming and Anisotropic Meshes, with Application to Ice Sheet Dynamics. - Tobin Isaac; Georg Stadler; Omar Ghattas (SIAM J. Sci. Comput., 2015)

Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data. - Omar Ghattas; Tobin Isaac; Noémi Petra; Georg Stadler (VECPAR, 2016)

Research and Education in Computational Science and Engineering. - 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; et al. (CoRR, 2016)

A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems. - Alen Alexanderian; Noemi Petra; Georg Stadler; Omar Ghattas (SIAM J. Sci. Comput., 2016)

Mean-Variance Risk-Averse Optimal Control of Systems Governed by PDEs with Random Parameter Fields Using Quadratic Approximations. - Alen Alexanderian; Noemi Petra; Georg Stadler; Omar Ghattas (SIAM/ASA J. Uncertain. Quantification, 2017)

A Data Scalable Augmented Lagrangian KKT Preconditioner for Large-Scale Inverse Problems. - Nick Alger; Umberto Villa; Tan Bui-Thanh; Omar Ghattas (SIAM J. Sci. Comput., 2017)

Weighted BFBT Preconditioner for Stokes Flow Problems with Highly Heterogeneous Viscosity. - Johann Rudi; Georg Stadler; Omar Ghattas (SIAM J. Sci. Comput., 2017)

hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems. - Umberto Villa; Noemi Petra; Omar Ghattas (J. Open Source Softw., 2018)

A Randomized Maximum A Posteriori Method for Posterior Sampling of High Dimensional Nonlinear Bayesian Inverse Problems. - Kainan Wang; Tan Bui-Thanh; Omar Ghattas (SIAM J. Sci. Comput., 2018)

Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. - Peng Chen 024; Keyi Wu; Joshua Chen; Tom O'Leary-Roseberry; Omar Ghattas (NeurIPS, 2019)

Disentangled behavioural representations. - Amir Dezfouli; Hassan Ashtiani; Omar Ghattas; Richard Nock; Peter Dayan; Cheng Soon Ong (NeurIPS, 2019)

Taylor approximation and variance reduction for PDE-constrained optimal control under uncertainty. - Peng Chen 024; Umberto Villa; Omar Ghattas (J. Comput. Phys., 2019)

Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators. - Nick Alger; Vishwas Rao; Aaron Myers; Tan Bui-Thanh; Omar Ghattas (SIAM J. Sci. Comput., 2019)

Projected Stein Variational Gradient Descent. - Peng Chen 024; Omar Ghattas (NeurIPS, 2020)

Frontera: The Evolution of Leadership Computing at the National Science Foundation. - Dan Stanzione; John West 001; R. Todd Evans; Tommy Minyard; Omar Ghattas; Dhabaleswar K. Panda 001 (PEARC, 2020)

Low Rank Saddle Free Newton: Algorithm and Analysis. - Thomas O'Leary-Roseberry; Nick Alger; Omar Ghattas (CoRR, 2020)

Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training. - Thomas O'Leary-Roseberry; Omar Ghattas (CoRR, 2020)

Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate. - Amal Alghamdi; Marc Andre Hesse; Jingyi Chen; Omar Ghattas (CoRR, 2020)

Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs. - Ilona Ambartsumyan; Wajih Bou Karam; Tan Bui-Thanh; Omar Ghattas; David E. Keyes; Georg Stadler; George Turkiyyah; Stefano Zampini (CoRR, 2020)

A fast and scalable computational framework for large-scale and high-dimensional Bayesian optimal experimental design. - Keyi Wu; Peng Chen 024; Omar Ghattas (CoRR, 2020)

Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. - Thomas O'Leary-Roseberry; Umberto Villa; Peng Chen 024; Omar Ghattas (CoRR, 2020)

Energy-conserving 3D elastic wave simulation with finite difference discretization on staggered grids with nonconforming interfaces. - Longfei Gao; Omar Ghattas; David E. Keyes (CoRR, 2020)

Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors. - Nick Alger; Peng Chen 024; Omar Ghattas (SIAM J. Sci. Comput., 2020)

Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs. - Ilona Ambartsumyan; Wajih Halim Boukaram; Tan Bui-Thanh; Omar Ghattas; David E. Keyes; Georg Stadler; George Turkiyyah; Stefano Zampini (SIAM J. Sci. Comput., 2020)

Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data Part II: Quantifying the Uncertainty. - Amal Alghamdi; Marc Andre Hesse; Jingyi Chen; Umberto Villa; Omar Ghattas (CoRR, 2021)

A fast and scalable computational framework for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placement. - Keyi Wu; Peng Chen 024; Omar Ghattas (CoRR, 2021)

Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data. - Thomas O'Leary-Roseberry; Xiaosong Du; Anirban Chaudhuri; Joaquim R. R. A. Martins; Karen Willcox; Omar Ghattas (CoRR, 2021)

Optimal design of acoustic metamaterial cloaks under uncertainty. - Peng Chen 024; Michael R. Haberman; Omar Ghattas (J. Comput. Phys., 2021)

Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters. - Peng Chen 024; Omar Ghattas (SIAM/ASA J. Uncertain. Quantification, 2021)

The imperative of physics-based modeling and inverse theory in computational science. - Karen E. Willcox; Omar Ghattas; Patrick Heimbach (Nat. Comput. Sci., 2021)

Stein Variational Reduced Basis Bayesian Inversion. - Peng Chen 024; Omar Ghattas (SIAM J. Sci. Comput., 2021)

hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs: Part I: Deterministic Inversion and Linearized Bayesian Inference. - Umberto Villa; Noemi Petra; Omar Ghattas (ACM Trans. Math. Softw., 2021)

Learning physics-based models from data: perspectives from inverse problems and model reduction. - Omar Ghattas; Karen Willcox (Acta Numer., 2021)

A GPU-Accelerated AMR Solver for Gravitational Wave Propagation. - Milinda Fernando; David Neilsen; Eric W. Hirschmann; Yosef Zlochower; Hari Sundar; Omar Ghattas; George Biros (SC, 2022)

Forward and inverse modeling of fault transmissibility in subsurface flows. - Jeonghun J. Lee; Tan Bui-Thanh; Umberto Villa; Omar Ghattas (Comput. Math. Appl., 2022)

Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. - Keyi Wu; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2022)

A stochastic Stein Variational Newton method. - Alex Leviyev; Joshua Chen; Yifei Wang 005; Omar Ghattas; Aaron Zimmerman (CoRR, 2022)

Interior over-stabilized enriched Galerkin methods for second order elliptic equations. - Jeonghun J. Lee; Omar Ghattas (CoRR, 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. - Lianghao Cao; Omar Ghattas; J. Tinsley Oden (SIAM J. Sci. Comput., 2022)

Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data. - Lianghao Cao; Keyi Wu; J. Tinsley Oden; Peng Chen 024; Omar Ghattas (CoRR, 2023)

Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels. - Nick Alger; Tucker Hartland; Noemi Petra; Omar Ghattas (CoRR, 2023)

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems. - Lianghao Cao; Thomas O'Leary-Roseberry; Prashant K. Jha; J. Tinsley Oden; Omar Ghattas (J. Comput. Phys., 2023)

Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm. - Dingcheng Luo; Lianghao Cao; Peng Chen 024; Omar Ghattas; J. Tinsley Oden (J. Comput. Phys., 2023)

An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement. - Keyi Wu; Peng Chen 024; Omar Ghattas (SIAM J. Sci. Comput., 2023)

A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design. - Keyi Wu; Peng Chen 024; Omar Ghattas (SIAM/ASA J. Uncertain. Quantification, 2023)

Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network. - Keyi Wu; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (J. Sci. Comput., 2023)

hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty. - Ki-Tae Kim; Umberto Villa; Matthew D. Parno; Youssef M. Marzouk; Omar Ghattas; Noemi Petra (ACM Trans. Math. Softw., 2023)

Interior over-penalized enriched Galerkin methods for second order elliptic equations. - Jeonghun J. Lee; Omar Ghattas (Comput. Math. Appl., 2023)

Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators. - Lianghao Cao; Thomas O'Leary-Roseberry; Omar Ghattas (CoRR, 2024)

Fast and Scalable FFT-Based GPU-Accelerated Algorithms for Hessian Actions Arising in Linear Inverse Problems Governed by Autonomous Dynamical Systems. - Sreeram Venkat; Milinda Fernando; Stefan Henneking; Omar Ghattas (CoRR, 2024)

Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs. - Dingcheng Luo; Joshua Chen; Peng Chen 024; Omar Ghattas (CoRR, 2024)

Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC. - Joseph Kirchhoff; Dingcheng Luo; Thomas O'Leary-Roseberry; Omar Ghattas (CoRR, 2024)

Real-time aerodynamic load estimation for hypersonics via strain-based inverse maps. - Julie Pham; Omar Ghattas; Noel Clemens; Karen Willcox (CoRR, 2024)

LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport. - Lianghao Cao; Joshua Chen; Michael Brennan; Thomas O'Leary-Roseberry; Youssef M. Marzouk; Omar Ghattas (CoRR, 2024)

Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport. - Ricardo Baptista; Lianghao Cao; Joshua Chen; Omar Ghattas; Fengyi Li; Youssef M. Marzouk; J. Tinsley Oden (J. Comput. Phys., 2024)

SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python. - Dingcheng Luo; Peng Chen 024; Thomas O'Leary-Roseberry; Umberto Villa; Omar Ghattas (J. Open Source Softw., 2024)

Point Spread Function Approximation of High-Rank Hessians with Locally Supported Nonnegative Integral Kernels. - Nick Alger; Tucker Hartland; Noemi Petra; Omar Ghattas (SIAM J. Sci. Comput., 2024)

Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning. - Thomas O'Leary-Roseberry; Peng Chen 024; Umberto Villa; Omar Ghattas (J. Comput. Phys., 2024)

Efficient PDE-Constrained Optimization Under High-Dimensional Uncertainty Using Derivative-Informed Neural Operators. - Dingcheng Luo; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (SIAM J. Sci. Comput., 2025)

Goal-Oriented Real-Time Bayesian Inference for Linear Autonomous Dynamical Systems With Application to Digital Twins for Tsunami Early Warning. - Stefan Henneking; Sreeram Venkat; Omar Ghattas (CoRR, 2025)

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors. - Dingcheng Luo; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2025)

Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone. - Stefan Henneking; Sreeram Venkat; Veselin Dobrev; John Camier; Tzanio V. Kolev; Milinda Fernando; Alice-Agnes Gabriel; Omar Ghattas (CoRR, 2025)

Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations. - Mathew Hu; Nick Alger; Rami Nammour; Omar Ghattas (CoRR, 2025)

Mixed-Precision Performance Portability of FFT-Based GPU-Accelerated Algorithms for Block-Triangular Toeplitz Matrices. - Sreeram Venkat; Kasia Swirydowicz; Noah Wolfe; Omar Ghattas (CoRR, 2025)