Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Overcoming the curse of dimensionality for approximating Lyapunov
  functions with deep neural networks under a small-gain condition   We propose a deep neural network architecture for storing approximate
Lyapunov functions of systems of ordinary differential equations. Under a
small-gain condition on the system, the number of neurons needed for an
approximation of a Lyapunov function with fixed accuracy grows only
polynomially in the state dimension, i.e., the proposed approach is able to
overcome the curse of dimensionality.

24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
33
The categories of this paper are: plasm-ph