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.

翻译：物理信息神经网络（PINNs）已成为解决逆问题的强大工具，尤其在系统完整信息未知且存在稀疏测量数据的情况下。该特性在血流动力学中尤为实用，因为边界信息通常难以建模，且高质量的血流测量数据往往难以获取。本研究采用PINNs方法，从升主动脉区域的二维含噪稀疏测量数据中，估计降阶模型参数并重构全速度场。结果表明，在模拟数据下该方法能实现稳定且准确的参数估计，而速度重构效果则依赖于测量质量与流场模式的复杂度。本方法可解决血流动力学及复杂耦合物理系统中具有临床意义的逆问题。