Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A deep learning method for solving Fokker-Planck equations   The time evolution of the probability distribution of a stochastic
differential equation follows the Fokker-Planck equation, which usually has an
unbounded, high-dimensional domain. Inspired by our early study in
\cite{li2018data}, we propose a mesh-free Fokker-Planck solver, in which the
solution to the Fokker-Planck equation is now represented by a neural network.
The presence of the differential operator in the loss function improves the
accuracy of the neural network representation and reduces the the demand of
data in the training process. Several high dimensional numerical examples are
demonstrated.

9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom