By Ahmed Attia and others

We develop a framework for goal-oriented optimal design of experiments (GOODE) for large-scale Bayesian linear inverse problems governed by PDEs. This framework differs from classical Bayesian optimal design of experiments (ODE) in the following sense: we seek experimental designs that minimize the posterior uncertainty in the experiment end-goal, e.g., a... Show more

June 11, 2018

Loading full text...

Similar articles

Loading recommendations...

Computational Engineering, Finance, and ScienceNumerical AnalysisOptimization and ControlApplications

x1

Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems

Click on play to start listening