CAREER: Forward and Inverse Uncertainty Quantification of Cardiovascular Fluid-Structure Dynamics via Multi-fidelity Physics-Informed Bayesian Geometric Deep Learning

点击翻译按钮获取中文摘要

Image-based computational models of the cardiovascular system play an increasingly important role in advancing the fundamental understanding of cardiovascular physiology and supporting clinical diagnosis and treatment planning. However, traditional models are primarily based on well-posed physics that are solved numerically, and their reliability is limited because of unknown or uncertain modeling conditions. On the other hand, sparse and noisy data have become increasingly available thanks to the rapid development of medical imaging techniques (e.g., flow MR images), which can be utilized for model inference and uncertainty reduction. Hence, forward uncertainty quantification and inverse data assimilation in cardiovascular simulations are of paramount importance to enhancing predictive confidence and prompting clinical translation efforts. This project will develop computational cyberinfrastructure for data-enabled forward and inverse stochastic cardiovascular modeling by leveraging recent advances in scientific machine learning. The project aims to establish a novel paradigm of data-augmented cardiovascular fluid-structure simulations, which could help transform personalized cardiovascular diagnostics/therapeutics, leading to higher quality of life. Moreover, this research program will also try to address long-standing challenges in effectively engaging students in STEM education across K-12, undergraduate, and graduate education by promoting an interactive and inclusive learning strategy. In particular, the PI will (1) design pedagogical software using physics-informed transfer learning for rapid interactive fluid simulation based on hand-drawn sketches; (2) develop new modules on Artificial Intelligence & Mechanics for U.S. Department of Education TRiO programs to engage K-12 students from low-income families in emerging interdisciplinary STEM fields. The overarching goal of this CAREER program is to pioneer a scalable and transformative computational cyberinfrastructure for forward and inverse uncertainty quantification (UQ) of cardiovascular modeling based on physics-informed Bayesian geometric deep learning, leveraging physics/physiological knowledge to enable efficient probabilistic learning with sparse and noisy data. This project tackles the fundamental challenges faced by the traditional paradigm of modeling cardiovascular fluid-structure interaction (FSI) dynamics. In the proposed framework, geometric deep learning models will be constructed based on both (partially) known physics and sparse measurement data in a Bayesian manner, enabling efficient forward and inverse FSI simulations with quantified uncertainties. Specifically, the PI will (1) formulate a variational PDE-informed, discretization-based learning framework using graph convolutional networks and use a reduced basis to constrain the dimension of the solution space, facilitating network training; (2) enable high-dimensional UQ capability of the proposed learning framework based on scalable variational Bayesian inference; (3) establish a multi-fidelity meta-learning strategy to parameterize solutions in the physical parameter space for rapid surrogate modeling, on the path to real-time cardiovascular simulations. The fast inference speed, strong expressibility, and GPU parallelization of deep learning models will be exploited to enable large-scale stochastic FSI simulations with patient-specific geometries. This project will build a solid foundation for developing the next-generation computational cyberinfrastructure of cardiovascular FSI modeling.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.