Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF
  Solutions   Optimal Power Flow (OPF) is a fundamental problem in power systems. It is
computationally challenging and a recent line of research has proposed the use
of Deep Neural Networks (DNNs) to find OPF approximations at vastly reduced
runtimes when compared to those obtained by classical optimization methods.
While these works show encouraging results in terms of accuracy and runtime,
little is known on why these models can predict OPF solutions accurately, as
well as about their robustness. This paper provides a step forward to address
this knowledge gap. The paper connects the volatility of the outputs of the
generators to the ability of a learning model to approximate them, it sheds
light on the characteristics affecting the DNN models to learn good predictors,
and it proposes a new model that exploits the observations made by this paper
to produce accurate and robust OPF predictions.

3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol