  We describe a one-dimensional (1D) unsteady and viscous flow model that is
derived from the momentum and mass conservation equations, and to enhance this
physics-based model, we use a machine learning approach to determine the
unknown modeling parameters. Specifically, we first construct an idealized
larynx model and perform ten cases of three-dimensional (3D) fluid--structure
interaction (FSI) simulations. The flow data are then extracted to train the 1D
flow model using a sparse identification approach for nonlinear dynamical
systems. As a result of training, we obtain the analytical expressions for the
entrance effect and pressure loss in the glottis, which are then incorporated
in the flow model to conveniently handle different glottal shapes due to vocal
fold vibration. We apply the enhanced 1D flow model in the FSI simulation of
both idealized vocal fold geometries and subject-specific anatomical geometries
reconstructed from the MRI images of rabbits' larynges. The 1D flow model is
evaluated in both of these setups and is shown to have robust performance.
Therefore, it provides a fast simulation tool superior to the previous 1D
models.
