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Entropy 2017, 19(2), 84;

Sequential Batch Design for Gaussian Processes Employing Marginalization †

Max-Planck-Institute for Plasma Physics, 85748 Garching, Germany
Author to whom correspondence should be addressed.
Academic Editor: Geert Verdoolaege
Received: 30 November 2016 / Revised: 16 February 2017 / Accepted: 17 February 2017 / Published: 21 February 2017
(This article belongs to the Special Issue Selected Papers from MaxEnt 2016)
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Within the Bayesian framework, we utilize Gaussian processes for parametric studies of long running computer codes. Since the simulations are expensive, it is necessary to exploit the computational budget in the best possible manner. Employing the sum over variances —being indicators for the quality of the fit—as the utility function, we establish an optimized and automated sequential parameter selection procedure. However, it is also often desirable to utilize the parallel running capabilities of present computer technology and abandon the sequential parameter selection for a faster overall turn-around time (wall-clock time). This paper proposes to achieve this by marginalizing over the expected outcomes at optimized test points in order to set up a pool of starting values for batch execution. For a one-dimensional test case, the numerical results are validated with the analytical solution. Eventually, a systematic convergence study demonstrates the advantage of the optimized approach over randomly chosen parameter settings. View Full-Text
Keywords: parametric studies; Gaussian process; parallelization; batch execution parametric studies; Gaussian process; parallelization; batch execution

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Preuss, R.; von Toussaint, U. Sequential Batch Design for Gaussian Processes Employing Marginalization †. Entropy 2017, 19, 84.

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