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CDS&E: Collaborative Research: Strategies for Managing Data in Uncertainty Quantification at Extreme Scales

CDS

基本信息

批准号：
1808576
负责人：
Tan Bui-Thanh
金额：
$ 40.98万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1808576&HistoricalAwards=false
关键词：
CDS&amp Collaborative Research Strategies Managing

项目摘要

The exponential increase in the quantity of measurements and data holds tremendous promise for data-driven scientific discovery and decision making. In many cases, data-driven scientific discovery is mathematically formulated as an inverse problem. For inverse problems that serve as a basis for discovery and decision-making for complex problems, the uncertainty in its solutions must be quantified. Though the past decades have seen tremendous advances in both theories and computational algorithms for inverse problems, quantifying the uncertainty (UQ) in their solutions taking big-data issues into account remains challenging. This is largely due to computationally demanding nature of existing mathematical techniques that are unable to scale up to the amount of data being generated. Consequently, much of the available data remains unused. This project develops UQ algorithms that are both computationally scalable as well as datascalable for making scientific progresses in geosciences and medical imaging. In particular, the proposed methods are evaluated in the context of two challenging data-driven applications: (1) from large amount of seismograms (records of the ground motion) perform geophysical imaging to infer earth's interior structure to better understand earthquakes, and (2) from magnetic resonance (MR) cine images of patients estimate the heart's function (e.g. motion, contraction) to detect early onset of heart disease (cardiomyopathy).The goal of this collaborative research project is to develop an integrated research program that addresses the data management and data analytics arising from both observations and scientific simulations, with applications from diverse domains at extreme scales. The project develops innovative statistical, mathematical, and parallel computational methods to manage the large amounts of simulation data as well as the ever increasing amounts of observation data required for extreme-scale UQ problems in general and Bayesian inverse problems in particular. These methods will be of immediate practical utility to scientists and engineers dealing with big data and large-scale UQ problems in sensing-based disciplines, geosciences, climatology, medical imaging, etc. The successful completion of the project would provide a first step towards the development of mathematical and computational methods for a wide range of data-driven large-scale inverse and UQ challenges that can lead to original scientific discoveries and promote the progress of science.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.

测量和数据数量的指数增长为数据驱动的科学发现和决策带来了巨大的希望。在许多情况下，数据驱动的科学发现在数学上被表述为一个逆问题。对于作为复杂问题发现和决策基础的反问题，其解的不确定性必须被量化。尽管在过去的几十年里，反问题的理论和计算算法都取得了巨大的进步，但考虑到大数据问题，量化他们解决方案中的不确定性(UQ)仍然具有挑战性。这在很大程度上是因为现有数学技术对计算的要求很高，无法扩大到生成的数据量。因此，许多可用的数据仍然没有被使用。该项目开发了UQ算法，这些算法既可以在计算上进行扩展，也可以在数据上进行扩展，以便在地球科学和医学成像方面取得科学进展。具体地说，所提出的方法是在两个具有挑战性的数据驱动应用的背景下进行评估的：(1)从大量的地震图(地面运动的记录)执行地球物理成像以推断地球内部结构以更好地了解地震，以及(2)从患者的磁共振(MR)电影图像估计心脏功能(例如运动、收缩)以检测早期心脏病(心肌病)。该合作研究项目的目标是开发一个集成的研究计划，以解决来自观测和科学模拟的数据管理和数据分析，并在极端尺度上使用来自不同领域的应用。该项目开发了创新的统计、数学和并行计算方法，以管理极端规模的UQ问题，特别是贝叶斯反问题所需的大量模拟数据和不断增加的观测数据。这些方法将对处理基于传感的学科、地球科学、气候学、医学成像等领域的大数据和大规模UQ问题的科学家和工程师立即产生实用价值。该项目的成功完成将为开发广泛的数据驱动的大规模逆和UQ挑战的数学和计算方法提供第一步，这些挑战可以导致原创性的科学发现，并促进科学的进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。