Open AccessMethodology for characterizing modeling and discretization uncertainties in computational simulation
Author(s) -
K. F. Alvin,
William L. Oberkampf,
Brian Milne Rutherford,
K.V. Diegert
Publication year - 2000
Language(s) - English
Resource type - Reports
DOI - 10.2172/752055
Subject(s) - discretization , computer science , discretization error , context (archaeology) , propagation of uncertainty , uncertainty quantification , inference , uncertainty analysis , process (computing) , bayesian inference , bayesian probability , mathematical optimization , algorithm , machine learning , mathematics , artificial intelligence , simulation , biology , operating system , mathematical analysis , paleontology
This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis