data consistent inversion

Convergence of Probability Densities Using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification

We analyze the convergence of probability density functions utilizing approximate models for both forward and inverse problems. We consider the standard forward uncertainty quantification problem where an assumed probability density on parameters is …

Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines

Specification of hydraulic conductivity as a model parameter in groundwater flow and transport equations is an essential step in predictive simulations. It is often infeasible in practice to characterize this model parameter at all points in space …

Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems

We formulate, and present a numerical method for solving, an inverse problem for inferring parameters of a deterministic model from stochastic observational data on quantities of interest. The solution, given as a probability measure, is derived …

A Stochastic Inverse Problem for Multiscale Models

Descriptions of complex multiscaled physical systems often involve many physical processes interacting through a multitude of scales. In many cases, the primary interest lies in predicting behavior of the system at the macroscale (i.e., engineering …

A Measure-Theoretic Algorithm for Estimating Bottom Friction in a Coastal Inlet: Case Study of Bay St. Louis during Hurricane Gustav (2008)

The majority of structural damage and loss of life during a hurricane is due to storm surge, thus it is important for communities in hurricane-prone regions to understand their risk due to surge. Storm surge in particular is largely influenced by …