171 citations · 211 across the 10 of their papers we have counts for
8 papers · 1 filter
A Scalable Interior-Point Gauss-Newton Method for PDE-Constrained Optimization with Bound Constraints
Tucker Hartland, Cosmin G. Petra, Noemi Petra +1
We present a scalable approach to solve a class of elliptic partial differential equation (PDE)-constrained optimization problems with bound constraints. This approach utilizes a r…
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 +1
We consider optimal design of infinite-dimensional Bayesian linear inverse problems governed by partial differential equations that contain secondary reducible model uncertainties,…
Statistical Treatment of Inverse Problems Constrained by Differential Equations-Based Models with Stochastic Terms
Emil M. Constantinescu, Noemi Petra, Julie Bessac +1
This paper introduces a statistical treatment of inverse problems constrained by models with stochastic terms. The solution of the forward problem is given by a distribution repres…
Estimation of the Robin coefficient field in a Poisson problem with uncertain conductivity field
Ruanui Nicholson, Noemi Petra, Jari Kaipio
We consider the reconstruction of a heterogeneous coefficient field in a Robin boundary condition on an inaccessible part of the boundary in a Poisson problem with an uncertain (or…
A Bayesian Approach for Parameter Estimation with Uncertainty for Dynamic Power Systems
Noemi Petra, Cosmin G. Petra, Zheng Zhang +2
We address the problem of estimating the uncertainty in the solution of power grid inverse problems within the framework of Bayesian inference. We investigate two approaches, an ad…
Mean-variance risk-averse optimal control of systems governed by PDEs with random parameter fields using quadratic approximations
Alen Alexanderian, Noemi Petra, Georg Stadler +1
We present a method for optimal control of systems governed by partial differential equations (PDEs) with uncertain parameter fields. We consider an objective function that involve…
A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems
Alen Alexanderian, Noemi Petra, Georg Stadler +1
We address the problem of optimal experimental design (OED) for Bayesian nonlinear inverse problems governed by PDEs. The goal is to find a placement of sensors, at which experimen…
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 +1
The majority of research on efficient and scalable algorithms in computational science and engineering has focused on the forward problem: given parameter inputs, solve the governi…