For a high number of dimensions, n > 3, it quickly becomes impossible to use traditional space filling experimental designs since the number of points needed grows exponentially. Instead data points must be chosen iteratively and intelligently, there where the information gain is the highest. This process is known as sequential design, adaptive sampling or active learning.
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.