Harnessing the Data Revolution in Space Physics: Topological Data Analysis and Deep Learning for Improved Solar Eruption Prediction

利用空间物理学中的数据革命:拓扑数据分析和深度学习以改进太阳喷发预测

基本信息

  • 批准号:
    2001670
  • 负责人:
  • 金额:
    $ 79.24万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2020
  • 资助国家:
    美国
  • 起止时间:
    2020-04-01 至 2024-08-31
  • 项目状态:
    已结题

项目摘要

Eruptions generated by sunspots --- large concentrations of magnetic field on the visible surface of the Sun --- can have a number of dire impacts on Earth-based technological systems, crippling satellites and power grids, among many other things. With enough advance notice, the effects of these events can be mitigated, but predicting them is a real challenge. In current operational practice, this is accomplished by human forecasters examining images of the Sun, classifying each sunspot according to a taxonomy developed in the 1960s, and then using look-up tables of historical probabilities to say whether or not it will erupt in the next 24 hours. Recently, there has been a burst of work on machine-learning methods to automate this task. To date, the "features" used in these approaches have been predominately physics-based: the gradient of the magnetic field, for instance, or the sum of its strength over high-flux regions. The main objective of this 3-year research project is to leverage algorithms based on the fundamental mathematics of shape --- topology and geometry --- to improve the performance of these methods. The specific plan is to use these powerful techniques to extend the relevant feature set to include characteristics of the magnetic field that are based purely on the geometry and topology of 2D magnetogram images. Although this approach ignores the 3D structure of the full electromagnetic fields, it can enhance the predictive skill of machine learning systems. Preliminary results show clear topological changes emerging in magnetograms of a 2017 sunspot more than 24 hours before it flared, as well as clear improvements in the accuracy scores of a neural-net based flare prediction method that employs these shape-based features. Better predictions of solar flares could allow operators of power grids, airlines, communications satellites, and other critical infrastructure systems to mitigate the effects of these potentially destructive events. The broader impacts of this project also include the development of the STEM workforce through the training of graduate students at the University of Colorado at Boulder, as well as education and outreach, including community lectures, development of large-scale, online courses and public lecture series. The interdisciplinary nature of the project will deepen the contact between the fields of space weather, applied mathematics, and computer science, bringing researchers, students, and post-docs from both fields into productive new collaborations. The collaboration with the Space Weather Technology, Research, and Education Center at the University of Colorado offers unique opportunities to factor in real-world forecasting constraints and set the stage for transitioning the results to operational status.For the first time, this 3-year research project would provide systematic quantitative measures of the shape of 2D magnetic structures in the Sun’s photosphere for the purposes of solar flare prediction. In a sense, this amounts to a mathematical systemization of the venerable McIntosh and Hale classification systems. This approach differs from current studies in the solar physics community that model the magnetic field-line structure: it uses topology to address the structure of two-dimensional sets. The analysis is restricted to photospheric magnetic field structures; the goal is to extract a formal characterization of shape that can be leveraged by machine learning to improve flare prediction. The considered addition of geometry into these methods by the project team is essential if they are to capture the full richness and physical relevance of the structures important in the evolution of a sunspot. This research project will point the way forward to a more robust set of features for machine-learning-based eruption prediction architectures. The research and EPO agenda of this project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research.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.
太阳黑子产生的喷发--太阳可见表面的大密度磁场--可能会对地球上的技术系统产生一些可怕的影响,使卫星和电网瘫痪,以及其他许多事情。如果事先得到足够的通知,这些事件的影响可以得到缓解,但预测它们是一个真正的挑战。在目前的操作实践中,这是通过人类预报员检查太阳图像,根据20世纪60年代开发的分类法对每个太阳黑子进行分类,然后使用历史概率查询表来判断它是否会在未来24小时内爆发来完成的。最近,在机器学习方法方面已经有了大量的工作来自动完成这项任务。到目前为止,这些方法中使用的“特征”主要是基于物理的:例如,磁场的梯度,或其在高通量区域的强度总和。这个为期3年的研究项目的主要目标是利用基于基本形状数学-拓扑学和几何学--的算法来提高这些方法的性能。具体计划是使用这些强大的技术来扩展相关特征集,以包括纯粹基于2D磁图图像的几何和拓扑的磁场特征。虽然这种方法忽略了整个电磁场的三维结构,但它可以提高机器学习系统的预测能力。初步结果显示,2017年太阳黑子爆发前超过24小时的磁图中出现了明显的拓扑变化,使用这些基于形状的特征的基于神经网络的耀斑预测方法的精度分数也有了明显的提高。对太阳耀斑的更好预测可以让电网、航空公司、通信卫星和其他关键基础设施系统的运营商减轻这些潜在破坏性事件的影响。该项目的更广泛影响还包括通过培训科罗拉多大学博尔德分校的研究生发展STEM工作队伍,以及教育和外联,包括社区讲座、开发大型在线课程和公共讲座系列。该项目的跨学科性质将加深空间气象、应用数学和计算机科学领域之间的联系,将这两个领域的研究人员、学生和博士后带入富有成效的新合作中。与科罗拉多大学空间气象技术、研究和教育中心的合作提供了独特的机会,将现实世界的预测限制因素考虑在内,并为将结果过渡到运行状态奠定了基础。这一为期3年的研究项目将首次为太阳耀斑预测提供系统的定量测量太阳光球层2D磁结构的形状。在某种意义上,这相当于将历史悠久的麦金托什和黑尔分类系统在数学上系统化。这种方法不同于目前太阳物理界对磁场线结构进行建模的研究:它使用拓扑学来解决二维集合的结构。分析仅限于光球磁场结构;其目标是提取可被机器学习用于改进耀斑预测的形状的形式特征。如果要捕捉太阳黑子演化中重要的结构的全部丰富性和物理相关性,项目团队考虑将几何学添加到这些方法中是必不可少的。这一研究项目将为基于机器学习的喷发预测体系结构提供一组更健壮的功能。该项目的研究和EPO议程支持AGS部门在发现、学习、多样性和跨学科研究方面的战略目标。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。

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)
Leveraging the mathematics of shape for solar magnetic eruption prediction
  • DOI:
    10.1051/swsc/2020014
  • 发表时间:
    2020-03
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    V. Deshmukh;T. Berger;E. Bradley;J. Meiss
  • 通讯作者:
    V. Deshmukh;T. Berger;E. Bradley;J. Meiss
Shape-based Feature Engineering for Solar Flare Prediction
  • DOI:
    10.1609/aaai.v35i17.17795
  • 发表时间:
    2020-12
  • 期刊:
  • 影响因子:
    0
  • 作者:
    V. Deshmukh;T. Berger;J. Meiss;E. Bradley
  • 通讯作者:
    V. Deshmukh;T. Berger;J. Meiss;E. Bradley
Oscillatory spreading and inertia in power grids
电网中的振荡传播和惯性
Comparing feature sets and machine-learning models for prediction of solar flares Topology, physics, and model complexity
  • DOI:
    10.1051/0004-6361/202245742
  • 发表时间:
    2023-06-19
  • 期刊:
  • 影响因子:
    6.5
  • 作者:
    Deshmukh, V.;Baskar, S.;Meiss, J. D.
  • 通讯作者:
    Meiss, J. D.
Solar flare catalog based on SDO/AIA EUV images: Composition and correlation with GOES/XRS X-ray flare magnitudes
  • DOI:
    10.3389/fspas.2022.1031211
  • 发表时间:
    2022-11
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Kiera van der Sande;N. Flyer;T. Berger;Riana Gagnon
  • 通讯作者:
    Kiera van der Sande;N. Flyer;T. Berger;Riana Gagnon
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Elizabeth Bradley其他文献

Simulating logic circuits: A multiprocessor application
Barriers to Hospice Admission: Results of a National Survey (417-A)
  • DOI:
    10.1016/j.jpainsymman.2010.10.123
  • 发表时间:
    2011-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Melissa Carlson;Elizabeth Bradley
  • 通讯作者:
    Elizabeth Bradley
Considerations for speech and language therapy management of dysphagia in patients who are critically ill with COVID-19: a single centre case series
COVID-19危重患者吞咽困难的言语和语言治疗管理注意事项:单中心病例系列
Unix Memory Allocations are Not Poisson
Unix 内存分配不是泊松分布
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    James Garnett;Elizabeth Bradley
  • 通讯作者:
    Elizabeth Bradley
A new method for choosing parameters in delay reconstruction-based forecast strategies
基于延迟重构的预测策略中参数选择的新方法
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    2.4
  • 作者:
    Joshua Garland;R. James;Elizabeth Bradley
  • 通讯作者:
    Elizabeth Bradley

Elizabeth Bradley的其他文献

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{{ truncateString('Elizabeth Bradley', 18)}}的其他基金

Computing Innovation Fellows Project 2021
2021 年计算创新研究员项目
  • 批准号:
    2127309
  • 财政年份:
    2021
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Continuing Grant
Computing Innovation Fellows 2020 Project
2020 年计算创新研究员项目
  • 批准号:
    2030859
  • 财政年份:
    2020
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Continuing Grant
The Shape of Data: A New Way to Detect Critical Shifts in System Performance
数据的形状:检测系统性能关键变化的新方法
  • 批准号:
    1537460
  • 财政年份:
    2015
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Standard Grant
EAGER: Characterizing Regime Shifts in Data Streams using Computational Topology - the Mathematics of Shape
EAGER:使用计算拓扑表征数据流中的政权转变 - 形状数学
  • 批准号:
    1447440
  • 财政年份:
    2014
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Standard Grant
INSPIRE: Automating Reasoning in Interpreting Climate Records of the Past
INSPIRE:解释过去气候记录的自动推理
  • 批准号:
    1245947
  • 财政年份:
    2012
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Standard Grant
Reduced-Order Dynamical Models for Effective Power Management in Computer Systems
计算机系统中有效电源管理的降阶动态模型
  • 批准号:
    1162440
  • 财政年份:
    2012
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Standard Grant
CSR---SMA: Validating Architectural Simulators Using Non-Linear Dynamics Techniques
CSR---SMA:使用非线性动力学技术验证建筑模拟器
  • 批准号:
    0720692
  • 财政年份:
    2007
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Continuing Grant
Collaborative Research: ITR: Software for Interpretation of Cosmogenic Isotope Inventories - Combination of Geology, Modeling, Software Engineering and Artificial Intelligence
合作研究:ITR:解释宇宙成因同位素库存的软件 - 地质学、建模、软件工程和人工智能的结合
  • 批准号:
    0325812
  • 财政年份:
    2003
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Standard Grant
ITR: An Interactive Experimental/Numerical Simulation System with Applications in MEMS Design
ITR:交互式实验/数值仿真系统在 MEMS 设计中的应用
  • 批准号:
    0083004
  • 财政年份:
    2000
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Continuing Grant
Automatic Construction of Accurate Models of Physical Systems
物理系统精确模型的自动构建
  • 批准号:
    9403223
  • 财政年份:
    1994
  • 资助金额:
    $ 79.24万
  • 项目类别:
    Standard Grant

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    面上项目
Molecular Interaction Reconstruction of Rheumatoid Arthritis Therapies Using Clinical Data
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    2010
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    2006
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    17.0 万元
  • 项目类别:
    青年科学基金项目

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RII Track-1: Harnessing the Data Revolution for Vermont: The Science of Online Corpora, Knowledge, and Stories (SOCKS)
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  • 批准号:
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