Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging

By J. Laurenceau, Pierre Sagaut. 2008

268 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7795424ba1a1860c4f5e9296bff5b293ad42aceb

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In Computational Theory and Mathematics

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Citations in the corpus

Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function (2013, unknown): “[18] [19] [20] [33] [34] [37]”
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems (2019, methodology): “Although great progress has already been made, the multi-ﬁdelity approaches based on GPR still have some limitations, e.g., approximations of discontinuous functions [7], high-dimensional problems [9], and inverse problems with strong nonlinearities (i.e., nonlinear partial diﬀerential equations) [8”
A Nonstationary Space-Time Gaussian Process Model for Partially Converged Simulations (2013, methodology): “A generalization of this model is proposed in Qian and Wu (2008); its application for optimization can be found in Han et al. (2010), Huang et al. (2006), Forrester et al. (2007), Laurenceau and Sagaut (2008) or Yamazaki and Mavriplis (2011).”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “The work [123] compares kriging and cokriging models on aerodynamic test functions.”
Performance study of multi-fidelity gradient enhanced kriging (2015, background): “Laurenceau and Sagaut (2008) studied an aerodynamic problem with direct and indirect formulations of GEK and concluded that indirect GEK outperforms direct GEK irrespective of their equivalent mathematical formulations.”
Review of multi-fidelity models (2016, background): “Laurenceau and Sagaut (2008) [125] compared the performance of Kriging and co-Kriging.”