15 citations · 15 across the 3 of their papers we have counts for
3 papers
A Distributions-based Approach for Data-Consistent Inversion
Kirana Bergstrom, Troy Butler, Tim Wildey
We formulate a novel approach to solve a class of stochastic problems, referred to as data-consistent inverse (DCI) problems, which involve the characterization of a probability me…
From Displacements to Distributions: A Machine-Learning Enabled Framework for Quantifying Uncertainties in Parameters of Computational Models
Taylor Roper, Harri Hakula, Troy Butler
This work presents novel extensions for combining two frameworks for quantifying both aleatoric (i.e., irreducible) and epistemic (i.e., reducible) sources of uncertainties in the…
Parameter Estimation with Maximal Updated Densities
Michael Pilosov, Carlos del-Castillo-Negrete, Tian Yu Yen +2
A recently developed measure-theoretic framework solves a stochastic inverse problem (SIP) for models where uncertainties in model output data are predominantly due to aleatoric (i…