Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Koopman Representations for Hybrid Systems   The Koopman operator lifts nonlinear dynamical systems into a functional
space of observables, where the dynamics are linear. In this paper, we provide
three different Koopman representations for hybrid systems. The first is
specific to switched systems, and the second and third preserve the original
hybrid dynamics while eliminating the discrete state variables; the second
approach is straightforward, and we provide conditions under which the
transformation associated with the third holds. Eliminating discrete state
variables provides computational benefits when using data-driven methods to
learn the Koopman operator and its observables. Following this, we use deep
learning to implement each representation on two test cases, discuss the
challenges associated with those implementations, and propose areas of future
work.

35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
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The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol