A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design

By Haitao Liu, Yew-Soon Ong, Jianfei Cai. Structural And Multidisciplinary Optimization, 2017

421 citations recorded by the source corpus.

Subfields: Building and Construction, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/2d25d2e8e8b1a02342a00378ef86c2adfc2a7703

How researchers see this paper

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

In Building and Construction

1. Yew-Soon Ong
2. Jianfei Cai
3. Haitao Liu

In Computational Theory and Mathematics

1. Xiaobo Shen
2. Jianfei Cai
3. Haitao Liu
4. Yew-Soon Ong
5. Sunil Gupta
6. Svetha Venkatesh
7. Santu Rana
8. Stewart Greenhill

In Industrial and Manufacturing Engineering

1. Yew-Soon Ong

Citations in the corpus

• Bayesian Optimization for Adaptive Experimental Design: A Review (2020, methodology): “Where the response is complex, non-parametric models are used, including Gaussian Processes, Multivariate Adaptive Regression Splines, and Support Vector Regression [4], [16], [17].”
• When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, background): “, active learning [8], multitask learning [9], [10], manifold learning [11], and optimization [12].”
• Remarks on multi-output Gaussian process regression (2018, background): “, discontinuous problems [52, 100]; (4) sampling strategies for sequential updating of the MOGPs by considering the output correlations and, particularly, the computing ratios between different fidelity levels in asymmetric scenarios [75, 101]; and (5) MOGP-assisted optimization [102, 103] and uncer”