Collaborative Research: CDS&E: Multifidelity Uncertainty Quantification Through Model Ensembles and Repositories

合作研究：CDS

• 批准号: 2104831
• 负责人: Daniele Schiavazzi
• 金额: $ 25.49万
• 依托单位: University of Notre Dame
• 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 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104831&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& Multifidelity; Uncertainty

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用于心血管疾病的诊断和治疗计划的临床研究和实践中，为可靠的模拟和分析工具创造了新的需求。量化这些模拟中的不确定性对于增加临床采用至关重要，但由于其过高的计算成本和复杂性，以前在很大程度上被忽视。为了应对这些挑战，该项目利用一类新的多保真度蒙特卡罗估计器来解决正问题和逆问题，旨在通过解决大量廉价的低保真度替代品来降低计算复杂性。它证明了所提出的方法在全面的临床问题，包括多个不确定性来源在一个合理的计算预算。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），包括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建模，心血管生理学，UQ和开源软件，使UQ在全尺寸3D心血管模拟中易于处理，利用多保真度估计器解决正问题和逆问题。该项目将产生无缝的网络基础设施，将两个备受推崇的开源软件包Dakota和SimVascular与相当大的用户社区连接起来。该项目正在为大规模的UQ任务创建新的网络基础设施生态系统。通过SimVascular（一个领先的心血管建模开源平台）向行业/学术界进行传播。它将利用SimVascular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学材料。虽然该项目侧重于心血管建模，但其结果可直接应用于其他工程问题。PI将在国家会议上组织小型座谈会和讲习班。他们将领导当地K-12学校的外联活动，以吸引女孩和代表性不足的少数民族（URM）学生参加STEM。PI将通过SURF计划指导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）小组指导女性学生。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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

Data-driven synchronization-avoiding algorithms in the explicit distributed structural analysis of soft tissue

软组织显式分布式结构分析中的数据驱动同步避免算法

• DOI: 10.1007/s00466-022-02248-w
• 发表时间: 2023
• 期刊: Computational Mechanics
• 影响因子: 4.1
• 作者: Tong, Guoxiang Grayson;Schiavazzi, Daniele E.
• 通讯作者: Schiavazzi, Daniele E.

Multifidelity data fusion in convolutional encoder/decoder networks

• DOI: 10.1016/j.jcp.2022.111666
• 发表时间: 2022-04
• 期刊: ArXiv
• 作者: Lauren Partin;G. Geraci;A. Rushdi;M. Eldred;D. Schiavazzi