用于流体动力学中流量测量指标高分辨率重建的物理信息神经网络
Physics-Informed Neural Networks for the High-Resolution Reconstruction of Flow Measurement Indicators in Fluid Dynamics
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中文总结 AI 辅助
该研究针对流体动力学中流量测量指标重建难题，提出物理信息神经网络（PINN）框架，将纳维-斯托克斯方程与实验流场数据结合，经计算机模拟和实验验证，能提升速度场重建及相关指标精度，比传统方法更接近真值。
中文摘要 AI 辅助
准确的、空间分辨的流场测量对于心血管研究和临床实践中血流动力学量的可靠评估至关重要。诸如4D流MRI、PIV或多普勒超声等实验技术往往产生稀疏、有噪声或分辨率不足的数据，这限制了诸如壁面剪应力等分布式或派生的血流动力学指标的保真度及其临床效用。为应对这些挑战，我们提出了一个物理信息神经网络（PINN）框架，将不可压缩的纳维-斯托克斯方程与来自实验流场数据的速度测量相结合。通过将物理定律嵌入数据中，PINN增强了速度场的重建，能够估计诸如压力和壁面剪应力等未测量的量，并提高了血流动力学指标的空间分辨率。我们使用计算机模拟和实验数据展示了我们方法的有效性。首先，我们将我们的方法应用于FDA喷嘴基准测试，利用控制粒子图像测速（PIV）测量和计算流体动力学（CFD）模拟。接下来，我们将我们的方法应用于动脉瘤模型中更复杂的血流情况，利用体外4D流MRI数据。在这两种情况下，数据驱动学习与基于物理的正则化之间的协同作用产生的结果比标准CFD或纯数据驱动方法更接近地面真值观测。我们的发现突出了PINN在提高分辨率不足的流场测量保真度和产生空间分辨的血流动力学指标方面的潜力。
英文摘要
Accurate, spatially resolved flow field measurements are essential for the reliable assessment of hemodynamic quantities in cardiovascular research and clinical practice. Experimental techniques, such as 4D flow MRI, PIV, or Doppler ultrasound, often yield data that are sparse, noisy, or under-resolved, particularly near vessel walls and in regions of complex flow. This limits the fidelity of distributed or derived hemodynamic indicators such as the wall shear stress and the clinical utility of such measurements. To address these challenges, we propose a physics-informed neural network (PINN) framework that integrates the incompressible Navier-Stokes equations with velocity measurements coming from experimental flow field data. By embedding physical laws into data, PINN enhances the reconstruction of velocity fields, enables the estimation of unmeasured quantities such as pressure and wall shear stress, and improves the spatial resolution of hemodynamic indicators. We show the effectiveness of our approach using both in silico and experimental data. First, we apply our method to the FDA nozzle benchmark, leveraging both control particle image velocimetry (PIV) measurements and computational fluid dynamics (CFD) simulations. Next, we apply our method to the more complex case of blood flow in an aneurysm model, exploiting in vitro 4D flow MRI data. In both cases, the synergy between data-driven learning and physics-based regularization yields results that align more closely with ground truth observations than standard CFD or pure data-driven approaches. Our findings highlight the potential of PINNs to improve the fidelity of under-resolved flow field measurements and yield spatially resolved hemodynamic indicators.
