Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Scalable Gaussian Process Modeling   Numerous engineering problems of interest to the industry are often
characterized by expensive black-box objective experiments or computer
simulations. Obtaining insight into the problem or performing subsequent
optimizations requires hundreds of thousands of evaluations of the objective
function which is most often a practically unachievable task. Gaussian Process
(GP) surrogate modeling replaces the expensive function with a
cheap-to-evaluate data-driven probabilistic model. While the GP does not assume
a functional form of the problem, it is defined by a set of parameters, called
hyperparameters. The hyperparameters define the characteristics of the
objective function, such as smoothness, magnitude, periodicity, etc. Accurately
estimating these hyperparameters is a key ingredient in developing a reliable
and generalizable surrogate model. Markov chain Monte Carlo (MCMC) is a
ubiquitously used Bayesian method to estimate these hyperparameters. At the GE
Global Research Center, a customized industry-strength Bayesian hybrid modeling
framework utilizing the GP, called GEBHM, has been employed and validated over
many years. GEBHM is very effective on problems of small and medium size,
typically less than 1000 training points. However, the GP does not scale well
in time with a growing dataset and problem dimensionality which can be a major
impediment in such problems. In this work, we extend and implement in GEBHM an
Adaptive Sequential Monte Carlo (ASMC) methodology for training the GP enabling
the modeling of large-scale industry problems. This implementation saves
computational time (especially for large-scale problems) while not sacrificing
predictability over the current MCMC implementation. We demonstrate the
effectiveness and accuracy of GEBHM with ASMC on four mathematical problems and
on two challenging industry applications of varying complexity.

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The categories of this paper are: plasm-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: econ
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org