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

CDS

基本信息

  • 批准号:
    1808652
  • 负责人:
  • 金额:
    $ 39.61万
  • 依托单位:
  • 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

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级增长为数据驱动的科学发现和决策提供了巨大的希望。 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量化。 尽管在过去的几十年里,逆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,量化其解决方案中的不确定性(UQ)仍然具有挑战性。 这在很大程度上是由于现有数学技术的计算要求很高,无法扩展到生成的数据量。 因此,许多现有数据仍然没有得到利用。 该项目开发了UQ算法,该算法在计算上可扩展,并且可扩展,以在地球科学和医学成像方面取得科学进展。特别是,在两个具有挑战性的数据驱动的应用程序的背景下评估所提出的方法:(1)从大量的地震记录来看,(记录地面运动)进行地球物理成像,以推断地球的内部结构,以更好地了解地震,(2)从磁共振(MR)电影图像的病人估计心脏的功能该合作研究项目的目标是开发一个综合研究计划,解决观察和科学模拟产生的数据管理和数据分析,并在极端规模下应用于不同领域。该项目开发了创新的统计,数学和并行计算方法,以管理大量的模拟数据,以及一般极端规模UQ问题和贝叶斯逆问题所需的不断增加的观测数据量。这些方法将立即实用于科学家和工程师处理大数据和大规模的UQ问题,在基于传感的学科,地球科学,气候学,医学成像,该项目的成功完成将为开发用于各种数据驱动的大型计算的数学和计算方法迈出第一步,该奖项反映了NSF的法定使命,并通过使用基金会的知识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

期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Multi-discretization domain specific language and code generation for differential equations
微分方程的多离散化域特定语言和代码生成
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Heisler, Eric;Deshmukh, Aadesh;Mazumder, Sandip;Sadayappan, Ponnuswamy;Sundar, Hari
  • 通讯作者:
    Sundar, Hari
Solving PDEs in space-time: 4D tree-based adaptivity, mesh-free and matrix-free approaches
求解时空偏微分方程:基于 4D 树的自适应性、无网格和无矩阵方法
  • DOI:
    10.1145/3295500.3356198
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ishii, Masado;Fernando, Milinda;Saurabh, Kumar;Khara, Biswajit;Ganapathysubramanian, Baskar;Sundar, Hari
  • 通讯作者:
    Sundar, Hari
Scalable Adaptive PDE Solvers in Arbitrary Domains
A scalable framework for adaptive computational general relativity on heterogeneous clusters
Finch: Domain Specific Language and Code Generation for Finite Element and Finite Volume in Julia
Finch:Julia 中有限元和有限体积的领域特定语言和代码生成
  • DOI:
    10.1007/978-3-031-08751-6_9
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    E. Heisler;Aadesh Deshmukh;H. Sundar
  • 通讯作者:
    H. Sundar
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Hari Sundar其他文献

TANGO: A GPU optimized traceback approach for sequence alignment algorithms
TANGO:用于序列比对算法的 GPU 优化回溯方法

Hari Sundar的其他文献

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{{ truncateString('Hari Sundar', 18)}}的其他基金

Collaborative Research: Accelerating the Pace of Discovery in Numerical Relativity by Improving Computational Efficiency and Scalability
协作研究:通过提高计算效率和可扩展性来加快数值相对论的发现步伐
  • 批准号:
    2207616
  • 财政年份:
    2022
  • 资助金额:
    $ 39.61万
  • 项目类别:
    Standard Grant
Collaborative Research: Engineering Fractional Photon Transfer for Random Laser Devices
合作研究:随机激光器件的工程分数光子传输
  • 批准号:
    2110215
  • 财政年份:
    2021
  • 资助金额:
    $ 39.61万
  • 项目类别:
    Standard Grant
Collaborative Research: CDS&E: A framework for solution of coupled partial differential equations on heterogeneous parallel systems
合作研究:CDS
  • 批准号:
    2004236
  • 财政年份:
    2020
  • 资助金额:
    $ 39.61万
  • 项目类别:
    Standard Grant
OAC Core: Small: Architecture and Network-aware Partitioning Algorithms for Scalable PDE Solvers
OAC 核心:小型:可扩展 PDE 求解器的架构和网络感知分区算法
  • 批准号:
    2008772
  • 财政年份:
    2020
  • 资助金额:
    $ 39.61万
  • 项目类别:
    Standard Grant
Collaborative Research: Massively Parallel Simulations of Compact Objects
协作研究:紧凑物体的大规模并行模拟
  • 批准号:
    1912930
  • 财政年份:
    2019
  • 资助金额:
    $ 39.61万
  • 项目类别:
    Standard Grant
CRII: CI: Scalable Multigrid Algorithms for Solving Elliptic PDEs on Power-Efficient Clusters
CRII:CI:用于求解节能集群上椭圆偏微分方程的可扩展多重网格算法
  • 批准号:
    1464244
  • 财政年份:
    2015
  • 资助金额:
    $ 39.61万
  • 项目类别:
    Standard Grant

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