An Efficient Multi-objective Optimization Algorithm Exploiting Gradient Enhanced Kriging with Optimally Selected Basis Functions for Electromagnetic Design

TL;DR: This chapter discusses the design and exploration of a Surrogate-based kriging model, and some of the techniques used in that process, as well as some new approaches to designing models based on the data presented.

TL;DR: This paper surveys their existing application in engineering design, and addresses the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes, along with recommendations for the appropriate use of statistical approximation techniques in given situations.

TL;DR: This work reviews the state-of-the-art metamodel-based techniques from a practitioner's perspective according to the role of meetamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems.

TL;DR: This paper compares Maximum Likelihood Estimation (MLE) and Cross-Validation (CV) parameter estimation methods for selecting a kriging model’s parameters given its form and and an R 2 of prediction and the corrected Akaike Information Criterion for assessing the quality of the created kriged model, permitting the comparison of different forms of a k Riging model.