Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points

By G. G. Wang. 2003

547 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cb3505730d406b2baae25b34fa3fa74058461592

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In Computational Theory and Mathematics

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, background): “Wang and colleagues developed the Adaptive Response Surface Method (ARSM), which systematically reduced the size of the design space by discarding portions of it that corresponded to objective function values larger than a given threshold value at each modeling-optimization iteration [45, 111].”
A new adaptive response surface method for reliability analysis (2013, background): “This enrichment is described now and is inspired from [46,7].”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “Wang (2003) partitioned the domain along each dimension according to the required sample size, and then sampled new points in the intervals with no existing ones.”
INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, methodology): “9 ≥ tf ≤ 5, The problem was optimized by the adaptive response surface method (ARSM) [83], CS [76], improved ARSM (IARSM) [83], and symbiotic organisms search (SOS) [75].”
Review of surrogate modeling in water resources (2012, background): “Examples of such techniques include: polynomials [Fen et al., 2009; Hussain et al., 2002; Myers and Montgomery, 2002; Wang, 2003], kriging, which is also sometimes referred to as design and analysis of computer experiment (DACE) in the surrogate modeling context [Sacks et al.”
Slime mould algorithm: A new method for stochastic optimization (2020, unknown): “Table 23 lists the optimization results for SMA compared to ARSM[83], SOS[80], CS[81] and IARSM[83].”
Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems (2011, methodology): “As it is seen, CS performs superior than the ARSM-based methods. θ1 1 2 2 1 3 θ2 2 1 P 2 2 P 1 2 2 1 3 1 P 2 P (a) (”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, background): “Wang [67] proposed an inheritable Latin Hypercube design for adaptive metamodeling.”