Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inferring Time-Dependent Distribution Functions from Kinematic Snapshots   We propose a method for constructing the time-dependent phase space
distribution function (DF) of a collisionless system from an isolated kinematic
snapshot. In general, the problem of mapping a single snapshot to a
time-dependent function is intractable. Here we assume a finite series
representation of the DF, constructed from the spectrum of the system's Koopman
operator. This reduces the original problem to one of mapping a kinematic
snapshot to a discrete spectrum rather than to a time-dependent function. We
implement this mapping with a convolutional neural network (CNN). The method is
demonstrated on two example models: the quantum simple harmonic oscillator, and
a self-gravitating isothermal plane. The latter system exhibits phase space
spiral structure similar to that observed in Gaia Data Release 2.

14
The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: bayes-an
3
The categories of this paper are: physics
1
The categories of this paper are: cs
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
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The categories of this paper are: stat
28
The categories of this paper are: alg-geom
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th