机器学习驱动的声传输损耗模拟代理
On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations
摘要：
代理模型是计算昂贵的模拟的基于数据的近似，其使得能够在许多物理领域中有效地探索模型的设计空间和明智的决策。然而，由于波动现象的非光滑、复杂行为，在振动声学领域中使用代理模型是具有挑战性的。本文研究了四种机器学习（ML）方法在声传输损失（STL）的替代建模。使用特征重要性和特征工程来提高模型的准确性，同时增加其可解释性和物理一致性。所提出的技术转移到其他问题的振动声学域和可能的局限性的模型进行了讨论。
英文摘要：
Surrogate models are data-based approximations of computationally expensive simulations that enable efficient exploration of the model's design space and informed decision-making in many physical domains. The usage of surrogate models in the vibroacoustic domain, however, is challenging due to the non-smooth, complex behavior of wave phenomena. This paper investigates four Machine Learning (ML) approaches in the modelling of surrogates of Sound Transmission Loss (STL). Feature importance and feature engineering are used to improve the models' accuracy while increasing their interpretability and physical consistency. The transfer of the proposed techniques to other problems in the vibroacoustic domain and possible limitations of the models are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
