Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural networks for topology optimization   In this research, we propose a deep learning based approach for speeding up
the topology optimization methods. The problem we seek to solve is the layout
problem. The main novelty of this work is to state the problem as an image
segmentation task. We leverage the power of deep learning methods as the
efficient pixel-wise image labeling technique to perform the topology
optimization. We introduce convolutional encoder-decoder architecture and the
overall approach of solving the above-described problem with high performance.
The conducted experiments demonstrate the significant acceleration of the
optimization process. The proposed approach has excellent generalization
properties. We demonstrate the ability of the application of the proposed model
to other problems. The successful results, as well as the drawbacks of the
current method, are discussed.

22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio