On-demand prediction of low-frequency average sound absorption coefficient of underwater coating using machine learning

• This study proposes an underwater coating with sound absorption ability in the middle-to-low frequency range. • The change of 20 sensitive parameters determine He 100,000 random sound absorption coefficient curves. • Deep neural networks are employed to predict the average value of the sound absorption coefficient curve. • Error between the expected and the predicted average absorption coefficient corresponding to the 20 sensitive parameters is only 0.026 % and 0.33 %. This study proposes an underwater coating with sound absorption ability in the middle-to-low frequency range and establishes an acoustic theoretical model combining the equivalent medium theory and the transfer matrix method. The sound…

• NN
  National Natural Science Foundation of China

  Awards: 52171323, 11704314
• CP
  China Postdoctoral Science Foundation

  Award: 2018M631194