Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Optimize Power Distribution Grids using Sensitivity-Informed
  Deep Neural Networks   Deep learning for distribution grid optimization can be advocated as a
promising solution for near-optimal yet timely inverter dispatch. The principle
is to train a deep neural network (DNN) to predict the solutions of an optimal
power flow (OPF), thus shifting the computational effort from real-time to
offline. Nonetheless, before training this DNN, one has to solve a large number
of OPFs to create a labeled dataset. Granted the latter step can still be
prohibitive in time-critical applications, this work puts forth an original
technique for improving the prediction accuracy of DNNs by taking into account
the sensitivities of the OPF minimizers with respect to the OPF parameters. By
expanding on multiparametric programming, it is shown that although inverter
control problems may exhibit dual degeneracy, the required sensitivities do
exist in general and can be computed readily using the output of any standard
quadratic program (QP) solver. Numerical tests showcase that
sensitivity-informed deep learning can enhance prediction accuracy in terms of
mean square error (MSE) by 2-3 orders of magnitude at minimal computational
overhead. Improvements are more significant in the small-data regime, where a
DNN has to learn to optimize using a few examples. Beyond multiparametric QPs,
the approach is currently being generalized to parametric (non)-convex
optimization problems.

30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
1
The categories of this paper are: cs