Predicting the output from a complex computer code when fast approximations are available

By Marc C. Kennedy, Anthony OʼHagan. 2000

1,657 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Statistics and Probability

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Citations in the corpus

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification (2018, methodology): “DNNs are also naturally suited for tasks for multilevel/multiﬁdelity UQ [73, 74, 75].”
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2019, methodology): “Namely, a deterministic or probabilistic input-output mapping is constructed using, e.g., polynomial basis functions [9], radial basis functions [10], Gaussian process (GP) [11, 12], and stochastic polynomial chaos expansion (PCE) [13–15], among others.”
A Provably Convergent Multifidelity Optimization Algorithm not Requiring High-Fidelity Derivatives (2010, methodology): “For the EGO implementation, 4n high-fidelity evaluations are used for the initial Latin hypercube sample and a genetic algorithm is used to maximize the expected improvement.”
Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy Experiments (2008, methodology): “The proposed model can be viewed as an extension of that used by Kennedy and O’Hagan (2000), where an autoregressive model is used as an adjustment model with a constant chosen for scale adjustment.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “This requirement allows to estimate the high/low-fidelity model hyperparameters individually (Kennedy and O’Hagan 2000).”
Quantile-Based Optimization of Noisy Computer Experiments With Tunable Precision (2013, methodology): “This situation has been addressed in the multifidelity literature [see Kennedy and O’Hagan (2000) and Qian and Wu (2008) for modeling; Forrester, Sóbester, and Keane (2007) and Huang et al. (2006) for optimization] and is not considered here, although many of the concepts presented here may apply…”
Calibration and Uncertainty Analysis for Computer Simulations with Multivariate Output (2008, methodology): “Other approaches that have been considered include techniques for combining simulations with different levels of complexity [13].”