Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion

By Sergey Oladyshkin, Wolfgang Nowak. Reliability Engineering & System Safety, 2012

453 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4508cf58285f5e8ed2eb130005f0d6f2c6db9a7b

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In Aerospace Engineering

In Statistical and Nonlinear Physics

In Statistics and Probability

Citations in the corpus

Optimizing User Satisfaction of Mobile Workloads Subject to Various Sources of Uncertainties (2019, methodology): “Non-parametric or data-driven methods for uncertainty quantification [13] are preferred.”
Polynomial chaos expansions for dependent random variables (2019, result): “This is consistent with the observations made in the univariate setting in [47].”
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems (2018, background): “However, aPC [25, 26, 27, 28, 29] is more suitable for building the orthogonal basis from arbitrary random space, without the need of any assumption on the distribution of the data.”
Arbitrary polynomial chaos expansion method for uncertainty quantification and global sensitivity analysis in structural dynamics (2020, background): “Because of its substantial generality in dealing with arbitrary probability 109 distributions, aPCE has been successfully applied to solve a wide range of engineering 110 problems [42-46].”
Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations (2015, methodology): “In order to implement the PCEs, the mode strengths should be estimated by intrusive or non-intrusive (i.e. black box) methods (Perez, 2008; Oladyshkin and Nowak, 2012).”
Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations (2013, methodology): “In [57, 60], the authors of the current study pointed out that the efforts invested in improved physical conceptualization, numerical codes, and stochastic modeling can easily be overwhelmed by error through human subjectivity in data interpretation at a very early stage of modeling, and they propos”