GEM: Explorative Global-To Kinetic-Scale Modeling of Collisionless Shocks Using Physics-Informed Data Mining and Machine Learning

GEM：使用物理信息数据挖掘和机器学习对无碰撞冲击进行探索性全局到动力学尺度建模

• 批准号: 2225463
• 负责人: Drew Turner
• 金额: $ 59.71万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225463&HistoricalAwards=false
• 关键词: GEM; Explorative; Global; Kinetic; Scale

Collisionless shock waves occur in space plasmas throughout the Universe and are one of the leading mechanisms considered responsible for accelerating cosmic rays. Understanding the physics at work in collisionless shocks in space plasmas has broad impacts on a number of fundamental scientific fields, from solar and space physics to planetary sciences and astrophysics. At Earth, a bow shock forms from the deflection of solar wind plasma around the planet's magnetic field, offering an ideal natural laboratory to explore the nature of such collisionless shocks. This effort promises to use state-of-the-art machine learning and artificial intelligence techniques and tools applied to 10,000 satellite crossings of Earth's bow shock. The intent is to develop a series of data-driven and physics-informed models of collisionless shocks that not only well capture the microscopic to macroscopic nature of Earth's own bow shock but may also be applied to a better understanding of shocks at other planetary systems (including exo-planets in other stellar systems), solar and stellar shocks, and other more extreme astrophysical shocks. If successful, these models will prove transformative by enabling us to produce and explore simulated examples of shocks in systems that we have no immediate access to, establishing a new interdisciplinary connection spanning between solar and magnetospheric space plasma physics and planetary and astrophysics. Furthermore, the effort involves a diverse leadership team and will provide dedicated research opportunities for underserved academic communities.The research centers around the development of and exploration using a pair of parameterized, empirical models of i) Earth's three-dimensional (3D), global bow shock, and ii) general collisionless shocks at ion-kinetic scales. Models will be developed using advanced data mining and state-of-the-art physics-informed machine learning tools applied to NASA's Magnetospheric Multiscale (MMS) and solar wind datasets. MMS' greater dataset is ideal for machine learning applications since it is accompanied by a dataset of "scientist-in-the-loop" (SITL) reports, which effectively serve as an expert-determined and easily minable validation dataset. Once developed as part of the research, model i) promises to be the first empirical (i.e., computationally inexpensive), parameterized, global-3D model of Earth's bow shock to accurately capture the critical quasi-parallel and quasi-perpendicular shock regimes and their respective distortion of the global bow shock surface. Model ii) will be developed by coupling machine learning applications to fundamental physical principles of collisionless shocks in space plasmas, establishing a genuine physics-informed machine learning model to ideally offer accurate predictions of not only shocks like those used to train the model (i.e., Earth's bow shock under a variety of driving conditions) but also extrapolative predictive capabilities for other planetary (and exoplanetary) bow shocks, collisionless solar shocks, interplanetary and heliospheric (and astrospheric) shocks, and other collisionless astrophysical shocks.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.

无碰撞冲击波发生在整个宇宙的空间等离子体中，被认为是加速宇宙射线的主要机制之一。了解空间等离子体中无碰撞冲击的物理作用对从太阳和空间物理学到行星科学和天体物理学等许多基础科学领域具有广泛的影响。在地球上，太阳风等离子体围绕地球磁场偏转形成弓形激波，为探索这种无碰撞激波的性质提供了理想的天然实验室。这一努力有望使用最先进的机器学习和人工智能技术和工具，应用于地球弓形激波的10，000次卫星穿越。其目的是开发一系列数据驱动和物理学知情的无碰撞冲击模型，不仅很好地捕捉地球自身弓形冲击的微观到宏观性质，而且还可以应用于更好地了解其他行星系统（包括其他恒星系统中的系外行星）的冲击，太阳和恒星冲击以及其他更极端的天体物理冲击。如果成功，这些模型将证明是变革性的，使我们能够制作和探索我们无法立即进入的系统中的冲击模拟实例，在太阳和磁层空间等离子体物理学与行星和天体物理学之间建立新的跨学科联系。此外，该项目的领导团队多元化，并将为服务不足的学术团体提供专门的研究机会。研究中心围绕着使用一对参数化的经验模型进行开发和探索，即i）地球的三维（3D）全球弓形激波，ii）离子动力学尺度的一般无碰撞激波。模型将使用先进的数据挖掘和最先进的物理学机器学习工具开发，这些工具适用于NASA的磁层多尺度（MMS）和太阳风数据集。MMS的更大的数据集是机器学习应用程序的理想选择，因为它伴随着“科学家在环”（SITL）报告的数据集，有效地充当专家确定和易于挖掘的验证数据集。一旦作为研究的一部分开发出来，模型i）有望成为第一个经验性的（即，计算成本低），参数化，全球3D模型的地球弓形激波，以准确地捕捉关键准平行和准垂直的冲击制度和各自的扭曲全球弓形激波表面。模型ii）将通过将机器学习应用程序与空间等离子体中无碰撞冲击的基本物理原理相结合来开发，建立一个真正的物理信息机器学习模型，以理想地提供不仅像用于训练模型的冲击那样的准确预测（即，该奖项反映了美国国家科学基金会的法定使命，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。