Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function

By Zhonghua Han, Stefan Görtz, Ralf Zimmermann. 2013

404 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/114a7d27fbd33715f0f24c50183b1531d350c26e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Max D. Gunzburger
2. Benjamin Peherstorfer
3. Joaquim R. Martins
4. Jianfei Cai
5. Haitao Liu
6. Yew-Soon Ong
7. Karen E. Willcox
8. John T. Hwang

In Statistical and Nonlinear Physics

1. Steven L. Brunton
2. Benjamin Peherstorfer
3. Karen E. Willcox
4. Max D. Gunzburger
5. J. Nathan Kutz
6. Bernd R. Noack
7. Joaquim R. Martins
8. Clarence W. Rowley

In Statistics and Probability

1. Benjamin Peherstorfer
2. Max D. Gunzburger
3. Joaquim R. Martins
4. Karen E. Willcox
5. Yew-Soon Ong
6. Jianfei Cai
7. Haitao Liu
8. Peter J. Schmid

Citations in the corpus

• Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling (2012, methodology): “In [19], this generalize hybrid bridge function was combined with gradient-enhanced kriging (GEK),with the gradients computed by adjointmethod [20]).”
• Remarks on multi-output Gaussian process regression (2018, methodology): “In order to improve the prediction quality, some works [27, 78] attempted to utilize the gradient information in the context of CoKG.”
• A Python surrogate modeling framework with derivatives (2019, methodology): “It then uses the differences between the high- and low-fidelity evaluations to construct a bridge function that corrects the low-fidelity model [21].”
• Recursive dynamic mode decomposition of transient and post-transient wake flows (2016, methodology): “1990), (3) provide a testbed for physical understanding (Lorenz 1963), (4) serve as computationally inexpensive surrogate models for optimization (Han et al. 2013) or (5) may be used as a plant for control design (Gerhard et al.”
• A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “The MFM frameworks, e.g., scaling function based modeling (Han et al. 2013; Zhou et al. 2017) and Bayesian multi-fidelity modeling (also called Co-Kriging) (Kennedy and O ’ Hagan 2000, 2001; Forrester et al. 2007; Qian and Wu 2008), have gained popularity in multidisciplinary design, optimization…”
• Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “In [97], cokriging with gradients and further developments of cokriging are compared for approximating aerodynamic models of airfoils.”