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math.NA2020
Learning Quantities of Interest from Dynamical Systems for Observation-Consistent Inversion
Steven Mattis, Kyle Robert Steffen, Troy Butler +2
Dynamical systems arise in a wide variety of mathematical models from science and engineering. A common challenge is to quantify uncertainties on model inputs (parameters) that cor…
math.PR2020
Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification: Extensions to
Troy Butler, Tim Wildey, Wenjuan Zhang
A previous study analyzed the convergence of probability densities for forward and inverse problems when a sequence of approximate maps between model inputs and outputs converges i…