4 papers
Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design
John D. Jakeman, Rebekah White, Bart van Bloemen Waanders +2
Traditional Bayesian optimal experimental design (OED) selects measurements that best inform a model's parameters. However, such measurements can be suboptimal for downstream predi…
Optimal Experimental Design Criteria for Data-Consistent Inversion
Troy Butler, John Jakeman, Michael Pilosov +2
The ability to design effective experiments is crucial for obtaining data that can substantially reduce the uncertainty in the predictions made using computational models. An optim…
Optimally balancing exploration and exploitation to automate multi-fidelity statistical estimation
Thomas Dixon, Alex Gorodetsky, John Jakeman +2
Multi-fidelity methods that use an ensemble of models to compute a Monte Carlo estimator of the expectation of a high-fidelity model can significantly reduce computational costs co…
A switching Kalman filter approach to online mitigation and correction of sensor corruption for inertial navigation
Artem Mustaev, Nicholas Galioto, Matt Boler +3
This paper introduces a novel approach to detect and address faulty or corrupted external sensors in the context of inertial navigation by leveraging a switching Kalman Filter comb…