gra2004-02

Parameter Space Exploration With Gaussian Process Trees

by:
Robert B. Gramacy, Herbert K. H. Lee, William MacReady

ABSTRACT

Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are unnecessary in regions where the response iseasy predicted; well-chosen designs could allow a mapping of the response with far fewer simulation runs. Thus, there is a need for computationally inexpensive surrogate models and an accompanying method for selecting smalldesigns. We explore a general methodology for addressing this need that uses non-stationary Gaussian processes. Binary trees partition the input space to facilitate non-stationarity and a Bayesian interpretation provides an explicit measure of predictive uncertainty that can be used to guide sampling. Our methods are illustrated on several examples, including a motivating example involving computational fluid dynamics simulation of a NASA reentry vehicle.

BIBTEX

@inproceedings{glm:04,      author = {R. B. Gramacy and Herbert K. H. Lee and William MacReady},      title = {Parameter Space Exploration With Gaussian Process Trees},      booktitle = {Proceedings of the International Conference on Machine Learning},      year = 2004,      pages = {353-360},      publisher = {Omnipress and ACM Digital Library},      url = {http://www.ams.ucsc.edu/~rbgramacy/papers/gra2004-02.pdf}      }

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