Uncertainty quantification

Uncertainty quantification (UQ) is the science of quantitative characterization and estimation of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if the speed was exactly known, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc., will lead to different results that can only be predicted in a statistical sense.

Many problems in the natural sciences and engineering are also rife with sources of uncertainty. Computer experiments on computer simulations are the most common approach to study problems in uncertainty quantification.^[1]^[2]^[3]^[4]

^ Sacks, Jerome; Welch, William J.; Mitchell, Toby J.; Wynn, Henry P. (1989). "Design and Analysis of Computer Experiments". Statistical Science. 4 (4): 409–423. doi:10.1214/ss/1177012413. JSTOR 2245858.
^ Iman, Ronald L.; Helton, Jon C. (1988). "An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models". Risk Analysis. 8 (1). Wiley: 71–90. Bibcode:1988RiskA...8...71I. doi:10.1111/j.1539-6924.1988.tb01155.x. ISSN 0272-4332.
^ Walker, W.E.; Harremoës, P.; Rotmans, J.; van der Sluijs, J.P.; van Asselt, M.B.A.; Janssen, P.; Krayer von Krauss, M.P. (2003). "Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support". Integrated Assessment. 4 (1). Swets & Zeitlinger Publishers: 5–17. Bibcode:2003IntAs...4....5W. doi:10.1076/iaij.4.1.5.16466. hdl:1874/386032. ISSN 1389-5176.
^ Ranftl, Sascha; von der Linden, Wolfgang (2021-11-13). "Bayesian Surrogate Analysis and Uncertainty Propagation". Physical Sciences Forum. 3 (1): 6. arXiv:2101.04038. doi:10.3390/psf2021003006. ISSN 2673-9984.

[1] Sacks, Jerome; Welch, William J.; Mitchell, Toby J.; Wynn, Henry P. (1989). "Design and Analysis of Computer Experiments". Statistical Science. 4 (4): 409–423. doi:10.1214/ss/1177012413. JSTOR 2245858.

[2] Iman, Ronald L.; Helton, Jon C. (1988). "An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models". Risk Analysis. 8 (1). Wiley: 71–90. Bibcode:1988RiskA...8...71I. doi:10.1111/j.1539-6924.1988.tb01155.x. ISSN 0272-4332.

[3] Walker, W.E.; Harremoës, P.; Rotmans, J.; van der Sluijs, J.P.; van Asselt, M.B.A.; Janssen, P.; Krayer von Krauss, M.P. (2003). "Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support". Integrated Assessment. 4 (1). Swets & Zeitlinger Publishers: 5–17. Bibcode:2003IntAs...4....5W. doi:10.1076/iaij.4.1.5.16466. hdl:1874/386032. ISSN 1389-5176.

[:0-4] Ranftl, Sascha; von der Linden, Wolfgang (2021-11-13). "Bayesian Surrogate Analysis and Uncertainty Propagation". Physical Sciences Forum. 3 (1): 6. arXiv:2101.04038. doi:10.3390/psf2021003006. ISSN 2673-9984.

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