The theory of Design of Experiments (DOE) describes methods and algorithms for optimally selecting data points from an n dimensional parameter space. A simple example, say you have to select 1000 points from a 3-dimensional space (n=3). This can be done randomly, using a full factorial design (an equal number of points in every dimension, i.e., 10x10x10) or according to a Latin hypercube. These are 3 basic examples of an experimental design.
Published in Chapter:
Grid Enabled Surrogate Modeling
Dirk Gorissen (Gent University–IBBT, Belgium), Tom Dhaene (Gent University–IBBT, Belgium), Piet Demeester (Gent University–IBBT, Belgium), and Jan Broeckhove (Gent University–IBBT, Belgium)
Copyright: © 2009
|Pages: 10
DOI: 10.4018/978-1-60566-184-1.ch025
Abstract
The simulation and optimization of complex systems is a very time consuming and computationally intensive task. Therefore, global surrogate modeling methods are often used for the efficient exploration of the design space, as they reduce the number of simulations needed. However, constructing such surrogate models (or metamodels) is often done in a straightforward, sequential fashion. In contrast, this chapter presents a framework that can leverage the use of compute clusters and grids in order to decrease the model generation time by efficiently running simulations in parallel. The authors describe the integration between surrogate modeling and grid computing on three levels: resource level, scheduling level and service level. This approach is illustrated with a simple example from aerodynamics.