Collaborative Research: Multifidelity Uncertainty Quantification Through Model Ensembles and Repositories

协作研究：通过模型集成和存储库进行多保真度不确定性量化

• 批准号: 2105345
• 负责人: Alison Marsden
• 金额: $ 50.87万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2105345&HistoricalAwards=false
• 关键词: Collaborative; Research; Multifidelity; Uncertainty; Quantification

Numerical simulations are increasingly used in clinical research and practice for diagnosis and treatment planning in cardiovascular disease, creating new demand for reliable simulation and analysis tools. Quantification of uncertainty in these simulations is crucial to increased clinical adoption but has previously been largely disregarded due to its excessive computational cost and complexity. To address these challenges, the project leverages a new class of multi-fidelity Monte Carlo estimators for direct and inverse problems, designed to mitigate computational complexity through the solution of a large number of inexpensive low-fidelity surrogates. It demonstrates the proposed approach in full-scale clinical problems including multiple uncertainty sources at a reasonable computational budget. The project’s main objective is to create an end-to-end advanced cyberinfrastructure ecosystem for uncertainty quantification (direct problem) and parameter estimation (inverse problem) in cardiovascular models incorporating realistic sources of uncertainty, able to leverage arbitrary low-fidelity models through advanced Monte Carlo estimators, while drastically reducing computational cost and complexity. The project’s interdisciplinary team is synergizing computational modeling, cardiovascular physiology, UQ and open-source software towards making UQ tractable in full-scale 3D cardiovascular simulations, leveraging multi-fidelity estimators for the solution of both direct and inverse problems. The project will produce seamless cyberinfrastructure linking two well-regarded open-source packages, Dakota and SimVascular, with sizable user communities.The project is creating new cyberinfrastructure ecosystems for large-scale UQ tasks. Dissemination to industry/academia is performed through SimVascular, a leading open-source platform for cardiovascular modeling. It will leverage SimVascular and the proposed multi-fidelity estimators to create hands-on teaching material for graduate and undergraduate courses. Although the project focuses on cardiovascular modeling, its results are directly applicable to other engineering problems. The PIs will organize minisymposia and workshops at national conferences. They will lead outreach activities to local K-12 schools to attract girls and underrepresented minority (URM) students to STEM. The PIs will mentor URM summer students through the SURF program and women students through the Women in Mathematics, Scientific Computing and Engineering (WiMSCE) group at Stanford.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.

数值模拟越来越多地应用于心血管疾病诊断和治疗计划的临床研究和实践，对可靠的模拟和分析工具产生了新的需求。这些模拟中不确定性的量化对于增加临床应用至关重要，但由于其过高的计算成本和复杂性，此前在很大程度上被忽视。为了应对这些挑战，该项目利用一类新型多保真蒙特卡洛估计器来解决正向和逆向问题，旨在通过解决大量廉价的低保真代理来降低计算复杂性。它展示了所提出的方法在全面的临床问题中的应用，包括在合理的计算预算下的多个不确定性源。该项目的主要目标是创建一个端到端的先进网络基础设施生态系统，用于心血管模型中的不确定性量化（直接问题）和参数估计（逆问题），纳入现实的不确定性来源，能够通过先进的蒙特卡罗估计器利用任意低保真度模型，同时大幅降低计算成本和复杂性。该项目的跨学科团队正在协同计算模型、心血管生理学、昆士兰大学和开源软件，使昆士兰大学能够进行全面的 3D 心血管模拟，利用多保真度估计器来解决正问题和逆问题。该项目将创建无缝的网络基础设施，将两个备受推崇的开源软件包 Dakota 和 SimVasular 与庞大的用户社区连接起来。该项目正在为大规模的昆士兰大学任务创建新的网络基础设施生态系统。通过 SimVasular（领先的心血管建模开源平台）向行业/学术界传播。它将利用 SimVasular 和拟议的多保真度估计器为研究生和本科生课程创建实践教材。尽管该项目的重点是心血管建模，但其结果可直接应用于其他工程问题。 PI 将在全国会议上组织小型研讨会和讲习班。他们将在当地 K-12 学校开展外展活动，以吸引女童和代表性不足的少数族裔 (URM) 学生学习 STEM。 PI 将通过 SURF 项目指导 URM 暑期学生，并通过斯坦福大学的女性数学、科学计算和工程 (WiMSCE) 小组指导女学生。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks

• DOI: 10.1016/j.cma.2021.114038
• 发表时间: 2020-09
• 期刊: Computer methods in applied mechanics and engineering
• 影响因子: 7.2
• 作者: Gabriel Maher;Casey M. Fleeter;D. Schiavazzi;A. Marsden

SimVascular Gateway for Education and Research

用于教育和研究的 SimVascular 网关

• DOI: 10.1145/3491418.3535162
• 发表时间: 2022
• 期刊: Practice and Experience in Advanced Research Computing
• 作者: Tran, Justin;Abeysinghe, Eroma;Ladisa, John;Marsden, Alison;Pierce, Marlon
• 通讯作者: Pierce, Marlon

svFSI: A Multiphysics Package for Integrated CardiacModeling

svFSI：用于集成心脏建模的多物理场软件包

• DOI: 10.21105/joss.04118
• 发表时间: 2022
• 期刊: Journal of Open Source Software
• 作者: Zhu, Chi;Vedula, Vijay;Parker, Dave;Wilson, Nathan;Shadden, Shawn;Marsden, Alison
• 通讯作者: Marsden, Alison

svMorph: Interactive Geometry-Editing Tools for Virtual Patient-Specific Vascular Anatomies

• DOI: 10.1115/1.4056055
• 发表时间: 2023-03-01
• 期刊: JOURNAL OF BIOMECHANICAL ENGINEERING-TRANSACTIONS OF THE ASME
• 影响因子: 1.7
• 作者: Pham，Jonathan;Wyetzner，Sofia;Marsden，Alison L. L.
• 通讯作者: Marsden，Alison L. L.