Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An MCMC Method for Uncertainty Set Generation via Operator-Theoretic
  Metrics   Model uncertainty sets are required in many robust optimization problems,
such as robust control and prediction with uncertainty, but there is no
definite methodology to generate uncertainty sets for nonlinear dynamical
systems. In this paper, we propose a method for model uncertainty set
generation via Markov chain Monte Carlo. The proposed method samples from
distributions over dynamical systems via metrics over transfer operators and is
applicable to general nonlinear systems. We adapt Hamiltonian Monte Carlo for
sampling high-dimensional transfer operators in a computationally efficient
manner. We present numerical examples to validate the proposed method for
uncertainty set generation.

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The categories of this paper are: q-bio
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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: dg-ga
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: physics
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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: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: cs