Modeling and Design Optimization of a Resonant Optothermoacoustic Trace Gas Sensor. - Noemi Petra; John Zweck; Susan E. Minkoff; Anatoliy Kosterev; James H. Doty III (SIAM J. Appl. Math., 2011)

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)

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)

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

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

Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms. - Emil M. Constantinescu; Noémi Petra; Julie Bessac; Cosmin G. Petra (SIAM/ASA J. Uncertain. Quantification, 2020)

Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know. - Alen Alexanderian; Noemi Petra; Georg Stadler; Isaac Sunseri (SIAM/ASA J. Uncertain. Quantification, 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)

Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty. - Alen Alexanderian; Ruanui Nicholson; Noemi Petra (CoRR, 2022)

Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initializaiton. - Tucker Hartland; Georg Stadler; Mauro Perego; Kim Liegeois; Noemi Petra (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)

On the implementation of a quasi-Newton interior-point method for PDE-constrained optimization using finite element discretizations. - Cosmin G. Petra; Miguel A. Salazar de Troya; Noemi Petra; Youngsoo Choi; Geoffrey M. Oxberry; Daniel A. Tortorelli (Optim. Methods Softw., 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)

Democratizing Uncertainty Quantification. - Linus Seelinger; Anne Reinarz; Mikkel Bue Lykkegaard; Amal Mohammed A. Alghamdi; David Aristoff; Wolfgang Bangerth; Jean Bénézech; Matteo Diez; Kurt Frey; John D. Jakeman; Jakob Sauer Jørgensen; Ki-Tae Kim; et al. (CoRR, 2024)

A Scalable Interior-Point Gauss-Newton Method for PDE-Constrained Optimization with Bound Constraints. - Tucker Hartland; Cosmin G. Petra; Noemi Petra; Jingyi Wang (CoRR, 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)

Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. - Terrence Alsup; Tucker Hartland; Benjamin Peherstorfer; Noemi Petra (Adv. Comput. Math., 2024)

Democratizing uncertainty quantification. - Linus Seelinger; Anne Reinarz; Mikkel Bue Lykkegaard; Robert Akers; Amal Mohammed A. Alghamdi; David Aristoff; Wolfgang Bangerth; Jean Bénézech; Matteo Diez; Kurt Frey; John D. Jakeman; Jakob Sauer Jørgensen; et al. (J. Comput. Phys., 2025)