4 papers
Multi-type Sensor Placement for PDE-based Bayesian Inverse Problems
Steven Maio, Alen Alexanderian, Karina Koval +1
We address optimal placement of multi-type sensors for Bayesian inverse problems governed by partial differential equations (PDEs). The proposed framework allows for sensors with d…
Subspace accelerated measure transport methods for fast and scalable sequential experimental design, with application to photoacoustic imaging
Tiangang Cui, Karina Koval, Roland Herzog +1
We propose a novel approach for sequential optimal experimental design (sOED) for Bayesian inverse problems involving expensive models with high-dimensional unknown parameters. Thi…
Non-intrusive optimal experimental design for large-scale nonlinear Bayesian inverse problems using a Bayesian approximation error approach
Karina Koval, Ruanui Nicholson
We consider optimal experimental design (OED) for nonlinear inverse problems within the Bayesian framework. Optimizing the data acquisition process for large-scale nonlinear Bayesi…
Tractable Optimal Experimental Design using Transport Maps
Karina Koval, Roland Herzog, Robert Scheichl
We present a flexible method for computing Bayesian optimal experimental designs (BOEDs) for inverse problems with intractable posteriors. The approach is applicable to a wide rang…