The following pages link to Omar Ghattas (Q221308):
Displaying 43 items.
- Stein Variational Reduced Basis Bayesian Inversion (Q4997362) (← links)
- 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 (Q5022488) (← links)
- hIPPYlib (Q5025234) (← links)
- HESSIAN-BASED SAMPLING FOR HIGH-DIMENSIONAL MODEL REDUCTION (Q5052353) (← links)
- Hierarchical Matrix Approximations of Hessians Arising in Inverse Problems Governed by PDEs (Q5132022) (← links)
- Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors (Q5146679) (← links)
- Taylor Approximation for Chance Constrained Optimization Problems Governed by Partial Differential Equations with High-Dimensional Random Parameters (Q5158925) (← links)
- Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators (Q5230654) (← links)
- Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems (Q5306460) (← links)
- Constructively well-posed approximation methods with unity inf–sup and continuity constants for partial differential equations (Q5326496) (← links)
- A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem (Q5348010) (← links)
- Weighted BFBT Preconditioner for Stokes Flow Problems with Highly Heterogeneous Viscosity (Q5372629) (← links)
- A Data Scalable Augmented Lagrangian KKT Preconditioner for Large-Scale Inverse Problems (Q5372667) (← links)
- Parallel Lagrange--Newton--Krylov--Schur Methods for PDE-Constrained Optimization. Part I: The Krylov--Schur Solver (Q5470337) (← links)
- 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 (Q5470338) (← links)
- An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement (Q5886849) (← links)
- Learning physics-based models from data: perspectives from inverse problems and model reduction (Q5887831) (← links)
- Nearly orthogonal two-dimensional grid generation with aspect ratio control (Q5951796) (← links)
- A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design (Q6109162) (← links)
- Bayesian model calibration for block copolymer self-assembly: likelihood-free inference and expected information gain computation via measure transport (Q6129922) (← links)
- Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate (Q6147036) (← links)
- Residual-based error correction for neural operator accelerated Infinite-dimensional Bayesian inverse problems (Q6147083) (← links)
- Interior over-penalized enriched Galerkin methods for second order elliptic equations (Q6149081) (← links)
- Optimal design of chemoepitaxial guideposts for the directed self-assembly of block copolymer systems using an inexact Newton algorithm (Q6158089) (← links)
- Large-scale Bayesian optimal experimental design with derivative-informed projected neural network (Q6159007) (← links)
- Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning (Q6202135) (← links)
- A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems (Q6255714) (← links)
- Sparse polynomial approximations for affine parametric saddle point problems (Q6307351) (← links)
- Inexact Newton Methods for Stochastic Nonconvex Optimization with Applications to Neural Network Training (Q6318870) (← links)
- hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems Governed by PDEs; Part I: Deterministic Inversion and Linearized Bayesian Inference (Q6324949) (← links)
- Optimal design of acoustic metamaterial cloaks under uncertainty (Q6345882) (← links)
- Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs (Q6354925) (← links)
- An efficient method for goal-oriented linear Bayesian optimal experimental design: Application to optimal sensor placemen (Q6360471) (← links)
- Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks (Q6385575) (← links)
- A computational framework for infinite-dimensional Bayesian inverse problems. II: stochastic Newton MCMC with application to ice sheet flow inverse problems (Q6486743) (← links)
- Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels (Q6510956) (← links)
- Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators (Q6525863) (← links)
- Point spread function approximation of high-rank Hessians with locally supported nonnegative integral kernels (Q6543105) (← links)
- hIPPYlib-MUQ: a Bayesian inference software framework for integration of data with complex predictive models under uncertainty (Q6601373) (← links)
- Gaussian mixture Taylor approximations of risk measures constrained by PDEs with Gaussian random field inputs (Q6740234) (← links)
- Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC (Q6740988) (← links)
- Real-time aerodynamic load estimation for hypersonics via strain-based inverse maps (Q6741992) (← links)
- LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport (Q6754154) (← links)
