Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-driven modelling of nonlinear dynamics by barycentric coordinates
  and memory   We present a numerical method to model dynamical systems from data. We use
the recently introduced method Scalable Probabilistic Approximation (SPA) to
project points from a Euclidean space to convex polytopes and represent these
projected states of a system in new, lower-dimensional coordinates denoting
their position in the polytope. We then introduce a specific nonlinear
transformation to construct a model of the dynamics in the polytope and to
transform back into the original state space. To overcome the potential loss of
information from the projection to a lower-dimensional polytope, we use memory
in the sense of the delay-embedding theorem of Takens. By construction, our
method produces stable models. We illustrate the capacity of the method to
reproduce even chaotic dynamics and attractors with multiple connected
components on various examples.

7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
37
The categories of this paper are: supr-con
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int