Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extensions to Multifidelity Monte Carlo Methods for Simulations of
  Chaotic Systems   Multifidelity Monte Carlo methods often rely on a preprocessing phase
consisting of standard Monte Carlo sampling to estimate correlation
coefficients between models of different fidelity to determine the weights and
number of samples for each level. For computationally intensive models, as are
often encountered in simulations of chaotic systems, this up-front cost can be
prohibitive. In this work, a correlation estimation procedure is developed for
the case in which the highest and next highest fidelity models are generated
via discretizing the same mathematical model using different resolution. The
procedure uses discretization error estimates to estimate the required
correlation coefficient without the need to sample the highest fidelity model,
which can dramatically decrease the cost of the preprocessing phase. The method
is extended to chaotic problems by using discretization error estimates that
account for the statistical nature of common quantities of interest and the
accompanying finite sampling errors that pollute estimates of such quantities
of interest. The methodology is then demonstrated on a model problem based on
the Kuramoto-Sivashinsky equation.

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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph