论文标题
在机器学习驱动的替代物中，用于传输损失模拟
On Machine Learning-Driven Surrogates for Sound Transmission Loss Simulations
论文作者
论文摘要
替代模型是基于数据昂贵的模拟的基于数据的近似值，可有效探索模型的设计空间并在许多物理领域中有明智的决策。然而，由于波浪现象的非平滑，复杂的行为，替代模型的使用是具有挑战性的。本文研究了声音传输损失替代物（STL）的建模中的四种机器学习（ML）方法。特征重要性和功能工程用于提高模型的准确性，同时提高其解释性和身体一致性。讨论了提出的技术转移到乙战域中其他问题的转移以及模型的可能局限性。
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.
