Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MO-PaDGAN: Generating Diverse Designs with Multivariate Performance
  Enhancement   Deep generative models have proven useful for automatic design synthesis and
design space exploration. However, they face three challenges when applied to
engineering design: 1) generated designs lack diversity, 2) it is difficult to
explicitly improve all the performance measures of generated designs, and 3)
existing models generally do not generate high-performance novel designs,
outside the domain of the training data. To address these challenges, we
propose MO-PaDGAN, which contains a new Determinantal Point Processes based
loss function for probabilistic modeling of diversity and performances. Through
a real-world airfoil design example, we demonstrate that MO-PaDGAN expands the
existing boundary of the design space towards high-performance regions and
generates new designs with high diversity and performances exceeding training
data.

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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
21
The categories of this paper are: adap-org