Department of Mechanical Engineering, Northern Arizona University, Flagstaff, AZ, USA.
Department of Mechanical, Materials and Aerospace Engineering, Illinois Institute of Technology, Chicago, IL, USA.
J R Soc Interface. 2021 Feb;18(175):20200802. doi: 10.1098/rsif.2020.0802. Epub 2021 Feb 10.
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.
高保真血流建模对于增强我们对心血管疾病的理解至关重要。尽管在计算和实验血流特性方面取得了重大进展，但由于参数存在不确定性、分辨率低和测量噪声，我们从这些研究中获得的知识仍然有限。此外，从这些数据集提取有用信息具有挑战性。数据驱动的建模技术有潜力克服这些挑战并改变心血管流动建模。在这里，我们回顾了几种数据驱动的建模技术，强调了在众多此类技术中出现的常见思想和原则，并提供了它们在心血管流体力学生产中的应用示例。特别是，我们讨论了主成分分析 (PCA)、鲁棒 PCA、压缩感知、数据同化的卡尔曼滤波器、低秩数据恢复以及心血管流动的其他几种降阶建模方法，包括动态模式分解和非线性动力学的稀疏识别。所有技术都在具有简单示例的心血管流动的上下文中呈现。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，最终着眼于数据驱动的患者特定血流建模。
