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Noemi Petra
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2020 – today
- 2025
- [j15]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, Ki-Tae Kim, Benjamin M. Kent, Massimiliano Martinelli, Matthew D. Parno, Riccardo Pellegrini, Noemi Petra, Nicolai André Brogaard Riis, Katherine Rosenfeld, Andrea Serani, Lorenzo Tamellini, Umberto Villa, Tim J. Dodwell, Robert Scheichl:
Democratizing uncertainty quantification. J. Comput. Phys. 521: 113542 (2025) - 2024
- [j14]Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer, Noemi Petra:
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. Adv. Comput. Math. 50(4): 65 (2024) - [j13]Nick Alger, Tucker Hartland, Noemi Petra
Point Spread Function Approximation of High-Rank Hessians with Locally Supported Nonnegative Integral Kernels. SIAM J. Sci. Comput. 46(3): 1658- (2024) - [i8]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, Massimiliano Martinelli, Matthew D. Parno, Riccardo Pellegrini, Noemi Petra, Nicolai André Brogaard Riis, Katherine Rosenfeld, Andrea Serani, Lorenzo Tamellini, Umberto Villa, Tim J. Dodwell, Robert Scheichl
Democratizing Uncertainty Quantification. CoRR abs/2402.13768 (2024) - [i7]Tucker Hartland, Cosmin G. Petra, Noemi Petra, Jingyi Wang:
A Scalable Interior-Point Gauss-Newton Method for PDE-Constrained Optimization with Bound Constraints. CoRR abs/2410.14918 (2024) - 2023
- [j12]Cosmin G. Petra
On the implementation of a quasi-Newton interior-point method for PDE-constrained optimization using finite element discretizations. Optim. Methods Softw. 38(1): 59-90 (2023) - [j11]Ki-Tae Kim
hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty. ACM Trans. Math. Softw. 49(2): 17:1-17:31 (2023) - [i6]Tucker Hartland, Georg Stadler, Mauro Perego, Kim Liegeois, Noemi Petra:
Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initializaiton. CoRR abs/2301.03644 (2023) - [i5]Nick Alger, Tucker Hartland, Noemi Petra, Omar Ghattas:
Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels. CoRR abs/2307.03349 (2023) - 2022
- [i4]Alen Alexanderian, Ruanui Nicholson, Noemi Petra:
Optimal design of large-scale nonlinear Bayesian inverse problems under model uncertainty. CoRR abs/2211.03952 (2022) - [i3]Terrence Alsup, Tucker Hartland, Benjamin Peherstorfer, Noémi Petra:
Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models. CoRR abs/2212.03366 (2022) - 2021
- [j10]Alen Alexanderian, Noemi Petra
Optimal Design of Large-scale Bayesian Linear Inverse Problems Under Reducible Model Uncertainty: Good to Know What You Don't Know. SIAM/ASA J. Uncertain. Quantification 9(1): 163-184 (2021) - [j9]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): 16:1-16:34 (2021) - [i2]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. CoRR abs/2112.00713 (2021) - 2020
- [j8]Emil M. Constantinescu
Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms. SIAM/ASA J. Uncertain. Quantification 8(1): 170-197 (2020)
2010 – 2019
- 2019
- [i1]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 abs/1909.03948 (2019) - 2018
- [j7]Umberto Villa, Noemi Petra, Omar Ghattas:
hIPPYlib: An Extensible Software Framework for Large-Scale Inverse Problems. J. Open Source Softw. 3(30): 940 (2018) - 2017
- [j6]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): 1166-1192 (2017) - 2016
- [j5]Alen Alexanderian
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) - [c1]Omar Ghattas, Tobin Isaac, Noémi Petra, Georg Stadler:
Scalable Algorithms for Bayesian Inference of Large-Scale Models from Large-Scale Data. VECPAR 2016: 3-6 - 2015
- [j4]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: 348-368 (2015) - 2014
- [j3]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) - [j2]Alen Alexanderian
A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized ℓ0-Sparsification. SIAM J. Sci. Comput. 36(5) (2014) - 2011
- [j1]Noemi Petra, John Zweck, Susan E. Minkoff, Anatoliy Kosterev, James H. Doty III:
Modeling and Design Optimization of a Resonant Optothermoacoustic Trace Gas Sensor. SIAM J. Appl. Math. 71(1): 309-332 (2011)
Coauthor Index
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