3 citations · 3 across the 5 of their papers we have counts for
6 papers
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…
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…
Glyph-Based Uncertainty Visualization and Analysis of Time-Varying Vector Fields
Timbwaoga A. J. Ouermi, Jixian Li, Zachary Morrow +2
Uncertainty is inherent to most data, including vector field data, yet it is often omitted in visualizations and representations. Effective uncertainty visualization can enhance th…
Hyper-differential sensitivity analysis with respect to model discrepancy: Mathematics and computation
Joseph Hart, Bart van Bloemen Waanders
Model discrepancy, defined as the difference between model predictions and reality, is ubiquitous in computational models for physical systems. It is common to derive partial diffe…