About: Uncertainty quantification

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Uncertainty quantification (UQ) is the science of quantitative characterization and reduction 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.

Attributes	Values
rdf:type	Thing work
rdfs:label	Uncertainty quantification (en)
rdfs:comment	Uncertainty quantification (UQ) is the science of quantitative characterization and reduction 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. (en)
rdfs:seeAlso	Inverse problem Propagation of uncertainty
foaf:depiction
dcterms:subject	Statistical theory Operations research Applied mathematics Mathematical modeling
Wikipage page ID	5987648 (xsd:integer)
Wikipage revision ID	1122741407 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayesian inference Bayesian probability Probability distribution Probabilistic numerics Bayes' theorem Statistical theory Hyperparameter Variance Confidence interval Covariance Mean Further research is needed Gaussian process Moment (mathematics) Monte Carlo method Computer experiment Kriging Macroeconomic policy Perturbation theory Polynomial chaos Surrogate model Fuzzy logic Least squares Operations research Drag (physics) Finite difference Normal distribution Partial differential equation Quantification of margins and uncertainties Regression analysis Reliability engineering Interval arithmetic Applied mathematics Mathematical modeling Karhunen–Loève theorem Free fall Frequentist probability Markov chain Monte Carlo Mathematical model Maximum likelihood Standard error Uncertainty Utility function Importance sampling Finite element method Multiplier uncertainty Statistical methods Random sample Bayesian regression Parameter estimate Prior distribution Posterior distribution Computer model Uncertainty propagation
sameAs	Uncertainty quantification Uncertainty quantification Uncertainty quantification
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thumbnail	wiki-commons:Special:FilePath/Bias_Correction.png?width=300
has abstract	Uncertainty quantification (UQ) is the science of quantitative characterization and reduction 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. (en)
gold:hypernym	Science
prov:wasDerivedFrom	wikipedia-en:Uncertainty_quantification?oldid=1122741407&ns=0
page length (characters) of wiki page	28066 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Uncertainty_quantification
is rdfs:seeAlso of	Uncertainty
is Link from a Wikipage to another Wikipage of	Bayesian probability Bayesian programming Probability box Robert Moser Scott A. Mitchell Probabilistic numerics Alison Tomlin Aretha Teckentrup DATADVANCE Salome (software) Christian Soize George Karniadakis Applied Biomathematics Computational materials science Computer experiment Computer simulation Pauline Barrieu Polynomial chaos Probability bounds analysis

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