Collaborative Research: Enabling Multi-Scale Studies of Magnetic Reconnection with Interpretable Data-Driven Models

合作研究：通过可解释的数据驱动模型实现磁重联的多尺度研究

• 批准号: 2108087
• 负责人: Matthew Edwards
• 金额: $ 44.02万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2108087&HistoricalAwards=false
• 关键词: Collaborative; Research; Enabling; Multi; Scale

This project will explore the multiscale physics of magnetic energy release in plasmas. Most of the visible matter in the universe is in the state of plasma and is magnetized. The magnetic energy stored in the plasma can be explosively released by magnetic reconnection –– a fundamental process that plays a key role in laboratory and astrophysical systems, from disruptions in fusion experiments, to spectacular solar flare events, to, potentially, the acceleration of very high-energy cosmic rays. The understanding of magnetic reconnection is challenging due to the complex interplay of different processes at many scales: from detailed physics of electron motion at very small scales, to plasma heating and flow generation at large scales, to high energy photon and particle acceleration that can carry away a large part of the available plasma energy. The goal of this project is to use machine learning techniques to unravel the connection between physics processes at small and large scales, and develop better multi-scale models of magnetic reconnection. In doing so, it will contribute to the goals of NSF's "Windows on the Universe: The Era of Multi-Messenger Astrophysics" Big Idea. The project will provide students and postdocs, including those from traditionally under-represented groups, with advanced training in basic plasma physics, computational physics, and machine learning, empowering them with a unique set of tools to address emerging scientific opportunities.The holistic understanding of magnetic reconnection requires the development of new coarse-grained models that can describe the macroscopic consequences of the essential kinetic physics of reconnection and particle acceleration. This is often referred to as the problem of finding good "closures"; that is, a reduced set of equations that capture the essential processes occurring on unresolved scales as a function of resolved quantities, and that can be solved in a computationally efficient way. Techniques from the field of machine learning are providing unique opportunities to harness the increasingly abundant data from experiments and high-fidelity simulations to accelerate the development of the required reduced physics models. The goal of this project is to develop and apply novel machine learning tools based on sparse and symbolic regression techniques to extract interpretable and generalizable reduced models from data of first-principles plasma simulations. Specifically, the project aims to construct better kinetic closures for magnetic reconnection; to derive better models of particle injection and acceleration by this fundamental plasma process; and to use this understanding to accelerate the development of multi-scale plasma algorithms. While the immediate focus will be on the problem of magnetic reconnection, the tools that will be developed are general and applicable to other areas of plasma physics, and more broadly to many-body phenomena. The development of these multiscale models can have a significant impact across different areas of plasma science, from fusion to space and astrophysical plasmas.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.

该项目将探索等离子体中磁能释放的多尺度物理学。宇宙中大部分可见物质都处于等离子体状态并且被磁化。 存储在等离子体中的磁能可以通过磁重联爆炸性释放——磁重联是一个在实验室和天体物理系统中发挥着关键作用的基本过程，从聚变实验的中断，到壮观的太阳耀斑事件，再到潜在的极高能宇宙射线的加速。由于许多尺度上不同过程的复杂相互作用，对磁重联的理解具有挑战性：从非常小尺度的电子运动的详细物理原理，到大尺度的等离子体加热和流动生成，再到可以带走大部分可用等离子体能量的高能光子和粒子加速。该项目的目标是利用机器学习技术来揭示小尺度和大尺度物理过程之间的联系，并开发更好的磁重联多尺度模型。这样做，它将有助于实现 NSF 的“宇宙之窗：多信使天体物理学时代”这一宏伟理念的目标。该项目将为学生和博士后（包括来自传统上代表性不足的群体的学生和博士后）提供基础等离子体物理、计算物理和机器学习方面的高级培训，使他们拥有一套独特的工具来应对新兴的科学机会。对磁重联的整体理解需要开发新的粗粒度模型，该模型可以描述重联和粒子加速的基本动力学物理的宏观后果。这通常被称为寻找良好“闭包”的问题；也就是说，一组简化的方程，捕获在未解析尺度上发生的基本过程作为解析量的函数，并且可以以计算有效的方式求解。机器学习领域的技术提供了独特的机会，可以利用来自实验和高保真模拟的日益丰富的数据来加速所需简化物理模型的开发。该项目的目标是开发和应用基于稀疏和符号回归技术的新型机器学习工具，从第一原理等离子体模拟的数据中提取可解释和可概括的简化模型。具体来说，该项目旨在构建更好的磁重联动力学闭合；通过这一基本等离子体过程推导出更好的粒子注入和加速模型；并利用这种理解来加速多尺度等离子体算法的开发。虽然当前的焦点将是磁重联问题，但将开发的工具是通用的，适用于等离子体物理学的其他领域，更广泛地适用于多体现象。这些多尺度模型的开发可以对等离子体科学的不同领域（从聚变到空间和天体物理等离子体）产生重大影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。