Physics-informed neural networks (PINNs) have emerged as a powerful tool for
solving inverse problems, especially in cases where no complete information
about the system is known and scatter measurements are available. This is
especially useful in hemodynamics since the boundary information is often
difficult to model, and high-quality blood flow measurements are generally hard
to obtain. In this work, we use the PINNs methodology for estimating
reduced-order model parameters and the full velocity field from scatter 2D
noisy measurements in the ascending aorta. The results show stable and accurate
parameter estimations when using the method with simulated data, while the
velocity reconstruction shows dependence on the measurement quality and the
flow pattern complexity. The method allows for solving clinical-relevant
inverse problems in hemodynamics and complex coupled physical systems.

利用 PINNs 方法从散射二维噪声测量中估计上行主动脉中的简化模型参数和全速度场，方法在模拟数据上表现出稳定且准确的参数估计，速度重建取决于测量质量和流动模式复杂性，可用于解决临床相关反问题和复杂耦合物理系统。