Efficient Sampling and Support Vector Regression for Multidisciplinary Design Optimization of Multistage Space Launch Vehicle

TL;DR: Researchers propose a rough set-based design methodology to reverse the classical design process, identifying feasible design regions meeting performance requirements, and multiple global optima, using Latinized Hypercube Sampling for complex high-dimensional problems.

TL;DR: A survey of recent publications in the field of aerospace where interest in multidisciplinary optimization has been particularly intense can be found in this paper, which includes sections on Mathematical Modeling, Design-oriented Analysis, Approximation Concepts, Optimization Procedures, System Sensitivity and Human Interface.

TL;DR: This paper investigates support vector regression (SVR) as an alternative technique for approximating complex engineering analyses and shows great potential for metamodeling applications, adding to the growing body of promising empirical performance of SVR.

TL;DR: Comparing and contrast five experimental design types and four approximation model types in terms of their capability to generate accurate approximations for two engineering applications with typical engineering behaviors and a wide range of nonlinearity reveals that uniform designs provide good sampling for generating accurate approxIMations using different sample sizes while kriging models provide accurate approxims that are robust for use with a variety of experimental designs and sample sizes.

TL;DR: A class of orthogonal Latin hypercubes that preserves orthogonality among columns is proposed, and can facilitate nonparametric fitting procedures, because one can select good space-filling designs within the class of OrthogonalLatinhypercubes according to selection criteria.