Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parameter-Conditioned Sequential Generative Modeling of Fluid Flows   The computational cost associated with simulating fluid flows can make it
infeasible to run many simulations across multiple flow conditions. Building
upon concepts from generative modeling, we introduce a new method for learning
neural network models capable of performing efficient parameterized simulations
of fluid flows. Evaluated on their ability to simulate both two-dimensional and
three-dimensional fluid flows, trained models are shown to capture local and
global properties of the flow fields at a wide array of flow conditions.
Furthermore, flow simulations generated by the trained models are shown to be
orders of magnitude faster than the corresponding computational fluid dynamics
simulations.

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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: eess
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an