Open Access

ARTICLE

Subdivision-Based Isogeometric BEM with Deep Neural Network Acceleration for Acoustic Uncertainty Quantification under Ground Reflection Effects

Yingying Guo¹, Ziyu Cui², Jibing Shen¹, Pei Li^3,*

1 Henan International Joint Laboratory of Structural Mechanics and Computational Simulation, School of Architectural Engineering, Huanghuai University, Zhumadian, 463000, China
2 College of Architecture and Civil Engineering, Xinyang Normal University, Xinyang, 464000, China
3 Centre for Industrial Mechanics, Institute of Mechanical and Electrical Engineering, University of Southern Denmark, Sønderborg, 6400, Denmark

* Corresponding Author: Pei Li. Email:

Computers, Materials & Continua 2025, 85(3), 4519-4550. https://doi.org/10.32604/cmc.2025.071504

Received 06 August 2025; Accepted 09 September 2025; Issue published 23 October 2025

Abstract

Accurate simulation of acoustic wave propagation in complex structures is of great importance in engineering design, noise control, and related research areas. Although traditional numerical simulation methods can provide precise results, they often face high computational costs when applied to complex models or problems involving parameter uncertainties, particularly in the presence of multiple coupled parameters or intricate geometries. To address these challenges, this study proposes an efficient algorithm for simulating the acoustic field of structures with adhered sound-absorbing materials while accounting for ground reflection effects. The proposed method integrates Catmull-Clark subdivision surfaces with the boundary element method (BEM). Subdivision surfaces generate smooth, high-quality meshes that accurately represent complex geometries, thereby enhancing the accuracy of acoustic analysis while avoiding excessive mesh refinement. To further reduce the computational burden associated with generating high-quality meshes and performing uncertainty quantification, a deep neural network (DNN) surrogate model is developed to accelerate calculations. Trained on BEM simulation data, the DNN can rapidly predict sound pressure responses under varying input parameters, significantly speeding up the overall simulation process and reducing computation time. Numerical examples demonstrate that the DNN surrogate model achieves high predictive accuracy while enabling fast and precise analysis of uncertainties in acoustic problems. These results indicate that the proposed approach provides a practical and efficient tool for engineering applications, facilitating rapid evaluations and design optimization in complex acoustic environments.

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Keywords

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