Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Application of Deep Learning in Generating Desired Design Options:
  Experiments Using Synthetic Training Dataset   Most design methods contain a forward framework, asking for primary
specifications of a building to generate an output or assess its performance.
However, architects urge for specific objectives though uncertain of the proper
design parameters. Deep Learning (DL) algorithms provide an intelligent
workflow in which the system can learn from sequential training experiments.
This study applies a method using DL algorithms towards generating demanded
design options. In this study, an object recognition problem is investigated to
initially predict the label of unseen sample images based on training dataset
consisting of different types of synthetic 2D shapes; later, a generative DL
algorithm is applied to be trained and generate new shapes for given labels. In
the next step, the algorithm is trained to generate a window/wall pattern for
desired light/shadow performance based on the spatial daylight autonomy (sDA)
metrics. The experiments show promising results both in predicting unseen
sample shapes and generating new design options.

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The categories of this paper are: physics
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph