Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inverse design optimization framework via a two-step deep learning
  approach: application to a wind turbine airfoil   The inverse approach is computationally efficient in aerodynamic design as
the desired target performance distribution is prespecified. However, it has
some significant limitations that prevent it from achieving full efficiency.
First, the iterative procedure should be repeated whenever the specified target
distribution changes. Target distribution optimization can be performed to
clarify the ambiguity in specifying this distribution, but several additional
problems arise in this process such as loss of the representation capacity due
to parameterization of the distribution, excessive constraints for a realistic
distribution, inaccuracy of quantities of interest due to theoretical/empirical
predictions, and the impossibility of explicitly imposing geometric
constraints. To deal with these issues, a novel inverse design optimization
framework with a two-step deep learning approach is proposed. A variational
autoencoder and multi-layer perceptron are used to generate a realistic target
distribution and predict the quantities of interest and shape parameters from
the generated distribution, respectively. Then, target distribution
optimization is performed as the inverse design optimization. The proposed
framework applies active learning and transfer learning techniques to improve
accuracy and efficiency. Finally, the framework is validated through
aerodynamic shape optimizations of the wind turbine airfoil. Their results show
that this framework is accurate, efficient, and flexible to be applied to other
inverse design engineering applications.

17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an