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

职业：通过多保真物理信息贝叶斯几何深度学习对心血管流体结构动力学进行正向和逆向不确定性量化

基本信息

批准号：
2047127
负责人：
Jian-Xun Wang
金额：
$ 52.78万
依托单位：
University of Notre Dame
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-15 至 2026-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047127&HistoricalAwards=false
关键词：
CAREER Forward Inverse Uncertainty Quantification

项目摘要

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.

心血管系统的基于图像的计算模型在促进对心血管生理学的基本了解和支持临床诊断和治疗计划方面起着越来越重要的作用。但是，传统模型主要基于良好的物理学，这些物理是在数值上解决的，并且由于未知或不确定的建模条件，其可靠性受到限制。另一方面，由于医学成像技术的快速开发（例如Flow MR图像），稀疏和嘈杂的数据已越来越多，可以将其用于模型推理和降低不确定性。因此，心血管模拟中的正向不确定性量化和逆数据同化对于增强预测信心和促进临床翻译工作至关重要。该项目将通过利用科学机器学习的最新进展来开发用于启用数据的前进和随机心血管建模的计算网络基础设施。该项目旨在建立一个新型的数据启动性心血管流体结构模拟范式，这可以帮助改变个性化的心血管诊断/治疗剂，从而提高生活质量。此外，该研究计划还将试图通过促进互动和包容性的学习策略来有效地吸引学生在K-12，本科和研究生教育的STEM教育方面的长期挑战。特别是，PI（1）使用物理信息传输学习设计教学软件，以基于手绘草图进行快速交互式流体模拟；（2）为美国教育部三人计划开发有关人工智能和力学的新模块，以吸引来自新兴跨学科STEM领域的低收入家庭的K-12学生。该职业课程的总体目标是开拓心血管建模的前进和反向不确定性量化（UQ），基于物理学中知情的贝叶斯几何深度学习，利用物理/生理知识，以启用有效的探究性学习和稀疏和Noisisy的数据。该项目解决了建模心血管流体结构互动（FSI）动力学的传统范式所面临的基本挑战。在拟议的框架中，将以贝叶斯的方式基于（部分）已知的物理学和稀疏测量数据来构建几何深度学习模型，从而实现具有量化不确定性的有效前进和逆FSI模拟。具体而言，PI（1）使用图形卷积网络制定一个基于跨PDE的，基于离散化的学习框架，并使用降低的基础来限制解决方案空间的维度，从而促进网络培训；（2）基于可扩展的贝叶斯推断，提出的学习框架的高维UQ能力；（3）建立一个多保真元学习策略，以在实时心血管模拟的道路上进行快速替代建模的物理参数空间中的解决方案进行参数化解决方案。将利用深度学习模型的快速推理速度，强表达和GPU并行化，以实现具有患者特异性几何形状的大规模随机FSI模拟。该项目将为心血管FSI建模的下一代计算网络基础架构建立稳固的基础。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来评估的值得支持的。