Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training neural networks under physical constraints using a stochastic
  augmented Lagrangian approach   We investigate the physics-constrained training of an encoder-decoder neural
network for approximating the Fokker-Planck-Landau collision operator in the
5-dimensional kinetic fusion simulation in XGC. To train this network, we
propose a stochastic augmented Lagrangian approach that utilizes pyTorch's
native stochastic gradient descent method to solve the inner unconstrained
minimization subproblem, paired with a heuristic update for the penalty factor
and Lagrange multipliers in the outer augmented Lagrangian loop. Our training
results for a single ion species case, with self-collisions and collision
against electrons, show that the proposed stochastic augmented Lagrangian
approach can achieve higher model prediction accuracy than training with a
fixed penalty method for our application problem, with the accuracy high enough
for practical applications in kinetic simulations.

22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
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
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th