6 papers
Hyper-differential sensitivity analysis with respect to model discrepancy: Sequential optimal experimental design
Madhusudan Madhavan, Joseph Hart, Bart van Bloemen Waanders
Large-scale optimization problems are ubiquitous in the physical sciences; yet, high-fidelity models can often be complex and computationally prohibitive for optimization. A practi…
Sampling through iterated approximation: Gradient-free and multi-fidelity Bayesian inference via transport
Daniel Sharp, Bart van Bloemen Waanders, Youssef Marzouk
We develop an iterative framework for Bayesian inference problems where the posterior distribution may involve computationally intensive models, intractable gradients, significant…
Preconditioned pseudo-time continuation for parameterized inverse problems
Joseph Hart, Alen Alexanderian, Bart van Bloemen Waanders
We consider parameterized variational inverse problems that are constrained by partial differential equations (PDEs). We seek to efficiently compute the solution of the inverse pro…
Path-OED for infinite-dimensional Bayesian linear inverse problems governed by PDEs
J. Nicholas Neuberger, Alen Alexanderian, Bart van Bloemen Waanders +1
We consider infinite-dimensional Bayesian linear inverse problems governed by time-dependent partial differential equations (PDEs) and develop a mathematical and computational fram…
Hyper-differential sensitivity analysis with respect to model discrepancy: Prior distributions
Joseph Hart, Bart van Bloemen Waanders, Jixian Li +2
Hyper-differential sensitivity analysis with respect to model discrepancy was recently developed to enable uncertainty quantification for optimization problems. The approach consis…
A control-oriented approach to optimal sensor placement
Madhusudan Madhavan, Alen Alexanderian, Arvind K. Saibaba +2
We propose a control-oriented optimal experimental design (cOED) approach for linear PDE-constrained Bayesian inverse problems. In particular, we consider optimal control problems…