A sampling technique enhancing accuracy and efficiency of metamodel-based RBDO: Constraint boundary sampling

By Tae Hee Lee, J. Jung. 2008

219 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7924413b432be133be293338d367da87c89492da

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. François Deheeger
2. Bruno Sudret
3. V. Dubourg
4. Jean-Marc Bourinet

In Environmental Engineering

1. Jean-Marc Bourinet
2. V. Dubourg
3. Bruno Sudret
4. François Deheeger

In Statistics and Probability

1. V. Dubourg
2. Bruno Sudret
3. François Deheeger
4. Jean-Marc Bourinet
5. Anthony Nouy
6. Xiaoping Du
7. Zhen Hu
8. Géraud Blatman

Citations in the corpus

• Reliability-based design optimization using kriging surrogates and subset simulation (2011, methodology): “Various refinement techniques have been proposed in the kriging-related literature, e.g. for global optimization (Jones et al., 1998) or for probability / quantile estimation (Oakley, 2004; Bichon et al., 2008; Lee and Jung, 2008; Ranjan et al., 2008; Vazquez and Bect, 2009; Picheny et al., 2010).”
• Metamodel-based importance sampling for structural reliability analysis (2011, background): “There exists a relatively large literature about the adaptive refinement of a Kriging prediction for accurate classification (or level-set approximation), see e.g. Oakley (2004); Lee and Jung (2008); Bichon et al. (2008); Vazquez and Bect (2009); Picheny et al. (2010); Echard et al. (2011).”