Error and uncertainty in modeling and simulation

By William Oberkampf, S. DeLand, B. Rutherford, K. Diegert, K. Alvin. Reliability Engineering & System Safety, 2002

660 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3732145099c4e1498bd00da63f511e77d59cd29f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence (2013, background): “Or, quoting Oberkampf and co-authors, 'realistic modelling and simulation of complex systems must include the non-deterministic features of the system and the environment' [2].”
An efficient method for reliability analysis under epistemic uncertainty based on evidence theory and support vector regression (2015, background): “In comparison to the objective characteristic of aleatory uncertainty, epistemic uncertainty is of subjective nature and it implies a potential inaccuracy in any phase and activity of the modelling process which is due to incomplete information and knowledge (Oberkampf et al. 2002).”
The rise of low-cost sensing for managing air pollution in cities. (2015, background): “Often modelling approaches are used to address this issue, but these might carry inherent aleatoric and epistemic uncertainties due to crude approximations in input conditions (Kumar et al. 2011; Oberkampf et al. 2002).”
Calibration, validation, and sensitivity analysis: What's what (2006, background): “Uncertainties in calculations have been discussed in many places (see, for example [30]).”
An exploration of alternative approaches to the representation of uncertainty in model predictions (2003, background): “…for a variable v with values from the interval [1] [10] and each of the following intervals assigned to a BPA of 0.1: [1] [3], [1] [4], [1] [10], [2] [4], [2] [6], [5] [8], [5] [10], [7] [8], [7] [10], [9] [10] . for y are defined by the sets of points CBF ¼ {½v; Bel Y ðY c v Þ; v [ Y} ¼ {½v;…”
Survey of sampling-based methods for uncertainty and sensitivity analysis (2006, background): “analysis verification [5,6], and (v) the importance of avoiding deliberately conservative assumptions if meaningful uncertainty and sensitivity analysis results are to be obtained [213–217].”
Implementation and evaluation of nonparametric regression procedures for sensitivity analysis of computationally demanding models (2009, background): “It is almost universally accepted that the sensitivity analysis (SA) and uncertainty analysis (UA) of these complex models is an important and necessary component to overall analyses [1, 2, 3, 4, 5].”