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.

无碰撞的无冲击波在整个宇宙的空间等离子体中发生，是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学对从太阳能和太空物理学到行星科学和天体物理学的许多基本科学领域产生广泛的影响。在地球上，弓令人冲击是由于地球磁场周围太阳风等离子体的偏转而形成的，它提供了理想的天然实验室，以探索这种无碰撞冲击的性质。这种努力有望使用最先进的机器学习和人工智能技术以及应用于10,000颗地球弓箭的卫星交叉口的工具。目的是开发一系列具有数据驱动的无碰撞冲击模型的模型，不仅可以很好地捕捉地球自身弓形冲击的宏观性质，而且还可以应用于对其他行星系统的冲击的更好理解（包括其他恒星系统中的Exo-Planets（包括其他恒星系统中的外观平面），Solar和Stellar Shocks Shocks Shocks Shocks selfsical Sarks和其他Arexsical Sarks and Sartsical sastical sallopsical和其他震惊。如果成功的话，这些模型将通过使我们能够在无法立即访问的系统中产生和探索模拟的冲击示例来证明具有变革性，从而建立了跨越太阳能和磁层空间等离子体物理学以及行星和天文学之间的新的跨学科连接。此外，这项努力涉及一个多样化的领导团队，并将为服务欠缺的学术社区提供专门的研究机会。研究中心围绕I）地球三维（3D），全球弓箭和II的参数化的经验模型的发展和探索中心。模型将使用高级数据挖掘和最先进的物理知识的机器学习工具应用于NASA的磁层多尺度（MMS）和太阳能风数据集。 MMS的更大数据集非常适合机器学习应用程序，因为它伴随着“科学家”（SITL）报告的数据集，该数据集有效地用作专家确定且易于最小的验证数据集。一旦作为研究的一部分开发，模型i）有望成为第一个经验（即计算廉价），参数化的，全球3D地球弓形冲击模型，以准确捕获关键的准准平行和准垂直的垂直冲击方案及其各自的全球弓击冲击表面的扭曲。 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冲击，行星际和地层（和天文圈）冲击以及其他无碰撞的天体冲击。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。