Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come?

372 citations recorded by the source corpus.

How researchers see this paper

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

Review of multi-fidelity models (2016, neutral): “These approximations were created to reduce computational cost when large number of expensive simulations are needed for such processes as optimization [54] [164] and uncertainty quantification (UQ) [122].”
Managing computational complexity using surrogate models: a critical review (2020, background): “Despite advances in computing power, the computational cost of sophisticated high-fidelity computer experiments often makes it impractical to rely exclusively on simulation for dealing with design problems (Viana et al. 2014).”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “The intuitive idea is to sample a new point by maximizing the mean square error (MMSE) (Jin et al. 2002) as Moreover, Sacks et al. (1989) (Sacks et al. 1989; Viana et al. 2014).”