Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bilinear residual Neural Network for the identification and forecasting
  of dynamical systems   Due to the increasing availability of large-scale observation and simulation
datasets, data-driven representations arise as efficient and relevant
computation representations of dynamical systems for a wide range of
applications, where model-driven models based on ordinary differential equation
remain the state-of-the-art approaches. In this work, we investigate neural
networks (NN) as physically-sound data-driven representations of such systems.
Reinterpreting Runge-Kutta methods as graphical models, we consider a residual
NN architecture and introduce bilinear layers to embed non-linearities which
are intrinsic features of dynamical systems. From numerical experiments for
classic dynamical systems, we demonstrate the relevance of the proposed
NN-based architecture both in terms of forecasting performance and model
identification.

26
The categories of this paper are: bayes-an
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc