Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A novel control method for solving high-dimensional Hamiltonian systems
  through deep neural networks   In this paper, we mainly focus on solving high-dimensional stochastic
Hamiltonian systems with boundary condition, which is essentially a Forward
Backward Stochastic Differential Equation (FBSDE in short), and propose a novel
method from the view of the stochastic control. In order to obtain the
approximated solution of the Hamiltonian system, we first introduce a
corresponding stochastic optimal control problem such that the extended
Hamiltonian system of the control problem is exactly what we need to solve,
then we develop two different algorithms suitable for different cases of the
control problem and approximate the stochastic control via deep neural
networks. From the numerical results, comparing with the Deep FBSDE method
developed previously from the view of solving FBSDEs, the novel algorithms
converge faster, which means that they require fewer training steps, and
demonstrate more stable convergences for different Hamiltonian systems.

19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol