Image-based computational fluid dynamics (CFD) modeling enables derivation of hemodynamic information, which has become a paradigm in cardiovascular research and healthcare. Nonetheless, the predictive accuracy largely depends on precisely specified boundary conditions and model parameters, which, however, are usually uncertain in most patient-specific cases. Quantifying the uncertainties in model predictions due to input randomness can provide predictive confidence and is critical to promote the transition of CFD modeling in clinical applications. In the meantime, forward propagation of input uncertainties often involves numerous expensive CFD simulations, which is computationally prohibitive in most practical scenarios. This paper presents an efficient bi-fidelity surrogate modeling framework for uncertainty quantification (UQ) in cardiovascular simulations, by leveraging the accuracy of high-fidelity models and efficiency of low-fidelity models. Contrary to most data-fit surrogate models with several scalar quantities of interest, this work aims to provide high-resolution, full-field predictions. Moreover, a novel empirical error bound estimation approach is introduced to evaluate the performance of the surrogate a priori. The proposed framework is tested on a number of vascular flows with both standardized and patient-specific vessel geometries, and different combinations of high- and low-fidelity models are investigated. The results show that the bi-fidelity approach can achieve high predictive accuracy with a significant reduction of computational cost, exhibiting its merit and effectiveness. Particularly, the uncertainties from a high-dimensional input space can be accurately propagated to clinically relevant quantities of interest in the patient-specific case using only a limited number of high-fidelity simulations, suggesting a good potential in practical clinical applications.
Inpainting physics: self-supervised learning for context-driven fluid simulation
AI for Image-Guided Diagnosis and Therapy, Technical University of Munich（图像引导诊断与治疗人工智能，慕尼黑技术大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；AI in Healthcare and Medicine, Technical University of Munich（医疗人工智能，慕尼黑技术大学）；Imperial College London（伦敦帝国学院）
Bifidelity Karhunen-Loève Expansion Surrogate with Active Learning for Random Fields
Department of Mechanical Engineering University of Michigan（机械工程系密歇根大学）；Sandia National Laboratories（沙伊国家实验室）；Guggenheim School of Aerospace Engineering Georgia Institute of Technology（盖茨基金会航空航天工程学院佐治亚理工学院）
