3 papers
math.OC2026
Iterative Data-Consistent Inversion with Multiple Push-forward Constraints
Tianyi Jiang, Troy Butler, Timothy Wildey +2
A foundational challenge in uncertainty quantification involves estimating a probability measure on the space of uncertain parameters such that its push-forward through a computati…
math.NA2025
Variational Data-Consistent Assimilation
Rylan Spence, Troy Butler, Clint Dawson
This work introduces a new class of four-dimensional variational data assimilation (4D-Var) methods grounded in data-consistent inversion (DCI) theory. The methods extend classical…
stat.ME2025
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…