论文标题
正常发音期间，基于深度学习的通用流量流量模型
A deep-learning based generalized reduced-order model of glottal flow during normal phonation
论文作者
论文摘要
本文提出了一个基于深度学习的广义还原模型（ROM），该模型（ROM）可以在正常发音期间快速准确地预测glottal流。该方法基于以下假设：声带的振动可以通过通用运动学方程（UKE）表示，该方程式用于生成震颤形状库。对于库中的每个形状，流速和压力分布的地面真实值是从高保真纳维尔 - 斯托克斯（N-S）解决方案中获得的。然后，对完全连接的深神经网络（DNN）进行训练，以在形状与流量和压力分布之间构建经验映射。获得的基于DNN的还原流量求解器与基于有限元方法（FEM）的实心动力学求解器耦合，以进行FSI模拟。通过与静态塑形和FSI模拟中的Navier-Stokes解决方案进行比较来评估降序模型。结果证明了准确性和效率的良好预测性能。
This paper proposes a deep-learning based generalized reduced-order model (ROM) that can provide a fast and accurate prediction of the glottal flow during normal phonation. The approach is based on the assumption that the vibration of the vocal folds can be represented by a universal kinematics equation (UKE), which is used to generate a glottal shape library. For each shape in the library, the ground truth values of the flow rate and pressure distribution are obtained from the high-fidelity Navier-Stokes (N-S) solution. A fully-connected deep neural network (DNN)is then trained to build the empirical mapping between the shapes and the flow rate and pressure distributions. The obtained DNN based reduced-order flow solver is coupled with a finite-element method (FEM) based solid dynamics solver for FSI simulation of phonation. The reduced-order model is evaluated by comparing to the Navier-Stokes solutions in both statics glottal shaps and FSI simulations. The results demonstrate a good prediction performance in accuracy and efficiency.
