6 papers
Copula Transformations for Data-Consistent Inversion
Troy Butler, Tianyi Jiang, João Silva +2
Data-consistent inversion (DCI) constructs probability measures whose push-forward distributions agree with observed data, while iterative data-consistent inversion (iDCI) extends…
A Data-Consistent Approach to Ensemble Filtering
Rylan Spence, Troy Butler, Clint Dawson
Ensemble data assimilation is challenging when the ensemble size is substantially smaller than the state and observation dimensions, leaving empirical covariance information rank d…
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…
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…
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…
Sequential Maximal Updated Density Parameter Estimation for Dynamical Systems with Parameter Drift
Carlos del-Castillo-Negrete, Rylan Spence, Troy Butler +1
We present a novel method for generating sequential parameter estimates and quantifying epistemic uncertainty in dynamical systems within a data-consistent (DC) framework. The DC f…