A sample-efficient deep learning method for multivariate uncertainty qualification of acoustic-vibration interaction problems

作者：Chen, Leilei; Cheng, Ruhui; Li, Shengze; Lian, Haojie^*; Zheng, Changjun; Bordas, Stephane P. A.

来源：Computer Methods in Applied Mechanics and Engineering, 2022, 393: 114784.

DOI：10.1016/j.cma.2022.114784

• FEM
• BEM coupling
• Isogeometric analysis
• Vibro-acoustic analysis
• Monte Carlo simulation
• Deep neural network
• SVD-RBF

We propose an efficient Monte Carlo simulation method to address the multivariate uncertainties in acoustic-vibration interaction systems. The deep neural network acts as a general surrogate model to enhance the sampling efficiency of Monte Carlo Simulation. Singular Value Decomposition - Radial Basis Functions (SVD-RBF) acts as a bridge between the original full model and the neural network, enabling the training datasets of the neural network to be evaluated rapidly from a reducedorder model. The snapshots of full order models are obtained with isogeometric analysis, in which we couple two numerical schemes for vibro-acoustic interaction problems: the isogeometric finite element method for simulating vibration of Kirchhoff- Love shells and isogeometric boundary element method for exterior acoustic waves. Numerical results show that the proposed algorithm can significantly improve the efficiency of uncertainty analysis.