Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to
  Reduce Predictive Uncertainty   Multi-fidelity Gaussian process is a common approach to address the extensive
computationally demanding algorithms such as optimization, calibration and
uncertainty quantification. Adaptive sampling for multi-fidelity Gaussian
process is a changing task due to the fact that not only we seek to estimate
the next sampling location of the design variable, but also the level of the
simulator fidelity. This issue is often addressed by including the cost of the
simulator as an another factor in the searching criterion in conjunction with
the uncertainty reduction metric. In this work, we extent the traditional
design of experiment framework for the multi-fidelity Gaussian process by
partitioning the prediction uncertainty based on the fidelity level and the
associated cost of execution. In addition, we utilize the concept of Believer
which quantifies the effect of adding an exploratory design point on the
Gaussian process uncertainty prediction. We demonstrated our framework using
academic examples as well as a industrial application of steady-state
thermodynamic operation point of a fluidized bed process

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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
3
The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: comp-gas
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The categories of this paper are: math