Reduced-order models (ROMs) allow for the simulation of blood flow in patient-specific vasculatures without the high computational cost and wait time associated with traditional computational fluid dynamics (CFD) models. Unfortunately, due to the simplifications made in their formulations, ROMs can suffer from significantly reduced accuracy. One common simplifying assumption is the continuity of static or total pressure over vascular junctions. In many cases, this assumption has been shown to introduce significant error. We propose a model to account for this pressure difference, with the ultimate goal of increasing the accuracy of cardiovascular ROMs. Our model successfully uses a structure common in existing ROMs in conjunction with machine-learning techniques to predict the pressure difference over a vascular bifurcation. We analyze the performance of our model on steady and transient flows, testing it on three bifurcation cohorts representing three different bifurcation geometric types. We also compare the efficacy of different machine-learning techniques and two different model modalities.

翻译：降阶模型（ROMs）能够在无需传统计算流体动力学（CFD）模型高计算成本与等待时间的情况下，模拟患者特异性血管中的血流。然而，由于其公式中的简化假设，ROMs的精度可能显著降低。一种常见的简化假设是血管分叉处静压或总压的连续性。在许多案例中，这一假设已被证实会引入显著误差。我们提出一种模型来解决这种压差问题，最终目标是提升心血管ROMs的精度。该模型成功利用现有ROMs中常见的结构，结合机器学习技术，预测血管分叉处的压差。我们分析了该模型在稳态与瞬态流动下的性能，并在代表三种不同分叉几何类型的三个分叉队列上进行了测试。我们还比较了不同机器学习技术及两种模型模态的有效性。