DOI: 10.1155/2024/7926619 ISSN: 1875-9203

Uncertainty Quantification of Vibroacoustics with Deep Neural Networks and Catmull–Clark Subdivision Surfaces

Zhongbin Zhou, Yunfei Gao, Yu Cheng, Yujing Ma, Xin Wen, Pengfei Sun, Peng Yu, Zhongming Hu
  • Mechanical Engineering
  • Mechanics of Materials
  • Geotechnical Engineering and Engineering Geology
  • Condensed Matter Physics
  • Civil and Structural Engineering

This study proposes an uncertainty quantification method based on deep neural networks and Catmull–Clark subdivision surfaces for vibroacoustic problems. The deep neural networks are utilized as a surrogate model to efficiently generate samples for stochastic analysis. The training data are obtained from numerical simulation by coupling the isogeometric finite element method and the isogeometric boundary element method. In the simulation, the geometric models are constructed with Catmull–Clark subdivision surfaces, and meantime, the physical fields are discretized with the same spline functions as used in geometric modelling. Multiple deep neural networks are trained to predict the sound pressure response for various parameters with different numbers and dimensions in vibroacoustic problems. Numerical examples are provided to demonstrate the effectiveness of the proposed method.

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