Adaptive Design and Analysis of Supercomputer Experiments

Abstract

Computer experiments often are performed to allow modeling of a response surface of a physical experiment that can be too costly or difficult to run except by using a simulator. Running the experiment over a dense grid can be prohibitively expensive, yet running over a sparse design chosen in advance can result in insufficient information in parts of the space, particularly when the surface calls for a nonstationary model. We propose an approach that automatically explores the space while simultaneously fitting the response surface, using predictive uncertainty to guide subsequent experimental runs. We use the newly developed Bayesian treed Gaussian process as the surrogate model; a fully Bayesian approach allows explicit measures of uncertainty. We develop an adaptive sequential design framework to cope with an asynchronous, random, agent–based supercomputing environment by using a hybrid approach that melds optimal strategies from the statistics literature with flexible strategies from the active learning literature. The merits of this approach are borne out in several examples, including the motivating computational fluid dynamics simulation of a rocket booster.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 239 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.