High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.

高保真血流模型对于增强我们对心血管疾病的了解至关重要。尽管血流的计算和实验表征取得了重大进展，但我们从此类研究中获得的知识仍然受到参数不确定性、低分辨率和测量噪声的限制。此外，从这些数据集中提取有用的信息具有挑战性。数据驱动的建模技术有可能克服这些挑战并改变心血管血流建模。在这里，我们回顾了几种数据驱动的建模技术，强调了众多此类技术中出现的共同想法和原则，并提供了如何在心血管流体力学背景下使用它们的说明性示例。特别是，我们讨论了主成分分析（PCA）、鲁棒PCA、压缩感知、用于数据同化的卡尔曼滤波器、低秩数据恢复以及用于心血管流降阶建模的几种其他方法，包括动态模式分解和非线性动力学的稀疏辨识。所有技术均在心血管流程的背景下通过简单的示例进行介绍。这些数据驱动的建模技术有可能改变计算和实验心血管研究，我们讨论了在该领域应用这些技术的挑战和机遇，最终寻求数据驱动的患者特异性血流建模。