Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A machine learning accelerated inverse design of underwater acoustic
  polyurethane coatings with cylindrical voids   Here, we report the development of a detailed "Materials Informatics"
framework for the design of acoustic coatings for underwater sound attenuation
through integrating Machine Learning (ML) and statistical optimization
algorithms with a Finite Element Model (FEM). The finite element models were
developed to simulate the realistic performance of the acoustic coatings based
on polyurethane (PU) elastomers with embedded cylindrical voids. The FEM
results revealed that the frequency-dependent viscoelastic behavior of the
polyurethane matrix has a significant impact on the magnitude and frequency of
the absorption peak associated with the cylinders at low frequencies, which has
been commonly ignored in previous studies on similar systems. The data
generated from the FEM was used to train a Deep Neural Network (DNN) to
accelerate the design process, and subsequently, was integrated with a Genetic
Algorithm (GA) to determine the optimal geometric parameters of the cylinders
to achieve maximized, broadband, low-frequency waterborne sound attenuation. A
significant, broadband, low-frequency attenuation is achieved by optimally
configuring the layers of cylindrical voids and using attenuation mechanisms,
including Fabry-P\'erot resonance and Bragg scattering of the layers of voids.
Integration of the machine learning technique into the optimization algorithm
further accelerated the exploration of the high dimensional design space for
the targeted performance. The developed DNN exhibited significantly increased
speed (by a factor of $4.5\times 10^3$ ) in predicting the absorption
coefficient compared to the conventional FEM(s). Therefore, the acceleration
brought by the materials informatics framework brings a paradigm shift to the
design and development of acoustic coatings compared to the conventional
trial-and-error practices.

22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol