Shock and Vibration (Jan 2024)

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

DOI
https://doi.org/10.1155/2024/7926619
Journal volume & issue
Vol. 2024

Abstract

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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.