Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling Systems with Machine Learning based Differential Equations   The prediction of behavior in dynamical systems, is frequently subject to the
design of models. When a time series obtained from observing the system is
available, the task can be performed by designing the model from these
observations without additional assumptions or by assuming a preconceived
structure in the model, with the help of additional information about the
system. In the second case, it is a question of adequately combining theory
with observations and subsequently optimizing the mixture. In this work, we
proposes the design of time-continuous models of dynamical systems as solutions
of differential equations, from non-uniform sampled or noisy observations,
using machine learning techniques. The performance of strategy is shown with
both, several simulated data sets and experimental data from Hare-Lynx
population and Coronavirus 2019 outbreack. Our results suggest that this
approach to the modeling systems, can be an useful technique in the case of
synthetic or experimental data.

27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph