Metamodels for simulation input-output relations
Russell R. Barton
- pp 289-299
Reads0
Chats0
TLDR
A state of the art review of recent developments in metarnodels, which discusses seven alternative modeling strategies that are active topics in the current literature.Abstract:
The simulation community has used metarnodels to study the behavior of computer simulations for over twenty-five years. The most popular teebniques have been based on parametric polynomial response surface approximations. In this state of the art review, we present recent developments in this area. We also discuss seven alternative modeling strategies that are active topics in the current literature.read more
Citations
More filters
Journal ArticleDOI
Metamodels for Computer-Based Engineering Design: Survey and Recommendations
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.
Journal ArticleDOI
Comparative studies of metamodelling techniques under multiple modelling criteria
TL;DR: This paper systematically compare four popular metamodelling techniques – polynomial regression, multivariate adaptive regression splines, radial basis functions, and kriging – based on multiple performance criteria using fourteen test problems representing different classes of problems.
Journal ArticleDOI
Kriging Models for Global Approximation in Simulation-Based Multidisciplinary Design Optimization
TL;DR: This work investigates the use of kriging models as alternatives to traditional second-order polynomial response surfaces for constructing global approximations for use in a real aerospace engineering application, namely, the design of an aerospike nozzle.
Proceedings ArticleDOI
Comparative studies of metamodeling techniques under multiple modeling criteria
TL;DR: This paper systematically compare four popular metamodeling techniques —Polynomial Regression, Multivariate Adaptive Regression Splines, Radial Basis Functions, and Kriging —based on multiple performance criteria using fourteen test problems representing different classes of problems.
References
More filters
Book
Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Book
A practical guide to splines
TL;DR: This book presents those parts of the theory which are especially useful in calculations and stresses the representation of splines as linear combinations of B-splines as well as specific approximation methods, interpolation, smoothing and least-squares approximation, the solution of an ordinary differential equation by collocation, curve fitting, and surface fitting.
Journal ArticleDOI
Generalized Linear Models
TL;DR: In this paper, the authors used iterative weighted linear regression to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.
Journal ArticleDOI
Orthonormal bases of compactly supported wavelets
TL;DR: This work construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity, by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction.
Journal ArticleDOI
Multivariate Adaptive Regression Splines
TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.