Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Frequency-Aware Reconstruction of Fluid Simulations with Generative
  Networks   Convolutional neural networks were recently employed to fully reconstruct
fluid simulation data from a set of reduced parameters. However, since
(de-)convolutions traditionally trained with supervised L1-loss functions do
not discriminate between low and high frequencies in the data, the error is not
minimized efficiently for higher bands. This directly correlates with the
quality of the perceived results, since missing high frequency details are
easily noticeable. In this paper, we analyze the reconstruction quality of
generative networks and present a frequency-aware loss function that is able to
focus on specific bands of the dataset during training time. We show that our
approach improves reconstruction quality of fluid simulation data in
mid-frequency bands, yielding perceptually better results while requiring
comparable training time.

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The categories of this paper are: math
10
The categories of this paper are: q-bio
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg