Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Introducing a Generative Adversarial Network Model for Lagrangian
  Trajectory Simulation   We introduce a generative adversarial network (GAN) model to simulate the
3-dimensional Lagrangian motion of particles trapped in the recirculation zone
of a buoyancy-opposed flame. The GAN model comprises a stochastic recurrent
neural network, serving as a generator, and a convoluted neural network,
serving as a discriminator. Adversarial training was performed to the point
where the best-trained discriminator failed to distinguish the ground truth
from the trajectory produced by the best-trained generator. The model
performance was then benchmarked against a statistical analysis performed on
both the simulated trajectories and the ground truth, with regard to the
accuracy and generalization criteria.

32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat