Efficient Optimization Design Method Using Kriging Model

By Shinkyu Jeong, M. Murayama, Kazuomi Yamamoto. 2005

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

Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor (2020, methodology): “using cheap approximated regression and classification models (like Gaussian process model [37], radial basis function network [38], etc.”
Convolutional Neural Networks for Steady Flow Approximation (2016, methodology): “Various surrogate models have been explored for design processes, such as polynomial regression [12, 3], multiple adaptive regression splines (MARS) [10], Kriging [12, 16], radial basis function network [19], and artificial neural networks [7, 4].”
Deep Probabilistic Surrogate Networks for Universal Simulator Approximation (2019, background): “, 2016), aerospace engineering (Jeong et al., 2005), material science (Rikards et al.”
Geodesic Convolutional Shape Optimization (2018, methodology): “We also plot the corresponding results of a standard GP-based method, also known as kriging (Jeong et al., 2005; Toal & Keane, 2011; Nardari et al., 2017; Xu et al., 2017), an industry standard as discussed in Section 2.1.”
Constrained Efficient Global Optimization of Expensive Black-box Functions (2022, background): “, in [11]) in machine learning and engineering literature.”
Building Efficient Response Surfaces of Aerodynamic Functions with Kriging and Cokriging (2008, methodology): “Numerous strategy exist [2] and have been applied to perform aerodynamic shape optimization [3, 4, 5].”