Physics-based 0D reduced-order models provide computationally lightweight predictions of cardiovascular flows, resolving bulk hemodynamics in fractions of a second that would take days to solve using traditional 3D finite-element techniques. However, the accuracy of 0D models is limited as a result of the dramatic simplifications made in their derivations. In this work, we use 0D parameters learned from high-fidelity 3D data to improve 0D model accuracy without sacrificing its low computational cost or interpretability. We use the resistor-quadratic resistor-inductor (RRI) model to predict pressure drops over 0D vessels and bifurcations, where the resistances and inductance (0D parameters) are predicted from the bifurcation or vessel geometry using neural networks trained on high-fidelity 3D simulations. We validate the hybrid physics-based data-driven framework in three types of patient-specific vasculature - aortic, aortofemoral, and pulmonary anatomies. Use of learned 0D parameters reduces error by at least 50% compared to baseline 0D parameters across all anatomical cohorts. The improvements are especially marked for the more complex pulmonary anatomies, where 0D models with learned parameters reduced error from 30% to 7%. Exclusion of the quadratic resistor in the RRI model improved convergence compared to using the full RRI model. The resulting hybrid model presents a means of real-time (personal laptop runtime of <2 seconds for the most complex pulmonary anatomies), interpretable, and accurate cardiovascular flow modeling, enabling digital twins that support clinical decision-making as well as cardiovascular science and engineering research.

翻译：基于物理的0维降阶模型能够提供计算轻量化的心血管血流预测，在数秒内即可解析整体血流动力学，而传统三维有限元方法需要数天才能完成。然而，由于推导过程中进行了大幅简化，0维模型的精度受到限制。本研究利用从高保真三维数据中学习的0维参数，在保留其低计算成本和可解释性的同时提升模型精度。我们采用电阻-二次电阻-电感（RRI）模型预测0维血管和分叉处的压降，其中电阻和电感（0维参数）通过神经网络基于高保真三维仿真数据从血管分叉或几何结构中学习得出。我们在三种患者特异性血管结构（主动脉、主-股动脉及肺动脉解剖结构）中验证了这一混合物理数据驱动框架。与基线0维参数相比，使用学习的0维参数在所有解剖队列中至少将误差降低了50%。在更为复杂的肺动脉解剖结构中，改进效果尤为显著——采用学习参数的0维模型将误差从30%降至7%。相较于完整RRI模型，排除二次电阻项提升了模型收敛性。最终构建的混合模型实现了实时（在个人笔记本电脑上，最复杂的肺动脉解剖结构运行时间<2秒）、可解释且精准的心血管血流建模，为支持临床决策及心血管科学与工程研究的数字孪生技术奠定了基础。