Kriging Hyperparameter Tuning Strategies

By David J. J. Toal, Neil W. Bressloff, Andy J. Keane. 2008

177 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/baf4b7fbab2f5bc19346d6141190b4a4377ad253

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. Felipe Antonio Chegury Viana
2. Alexander I. J. Forrester
3. Vladimir Balabanov
4. V. Toropov
5. Andy J. Keane
6. Timothy W. Simpson
7. Carren M. E. Holden
8. James M. Parr

In Management Science and Operations Research

1. Alexander I. J. Forrester
2. Felipe Antonio Chegury Viana
3. Vladimir Balabanov
4. V. Toropov
5. Andy J. Keane
6. Carren M. E. Holden
7. Timothy W. Simpson
8. James M. Parr

In Mechanical Engineering

1. Andy J. Keane
2. Alexander I. J. Forrester
3. Timothy W. Simpson

Citations in the corpus

• Recent advances in surrogate-based optimization (2009, unknown): “[77], Zhang and Leithead [87]).”
• Infill sampling criteria for surrogate-based optimization with constraint handling (2012, background): “Different tuning strategies have been examined by Toal et al. (2008), concluding that a so-called light tune strategy is adequate up to 12 variables, provided enough true simulations are performed.”
• Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, background): “the tuning of the kriging parameters [85,86], the update of the”
• Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling (2012, background): “The readers are referred to [34] for more hyper-parameter tuning strategies.”
• Globally Approximate Gaussian Processes for Big Data With Application to Data-Driven Metamaterials Design (2019, methodology): “Many global optimization methods such as genetic algorithm (GA) [40], pattern searches [41,42], and particle swarm optimization [43] have been employed to solve for ˆ ω in Eq.”