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理物理。宇宙中的大多数可见物质处于血浆状态，并被磁化。在血浆中存储的磁能可以通过磁重新连接爆炸性地释放，这是一个基本过程，在实验室和天体物理系统中起着关键作用，从融合实验的破坏到壮观的太阳火光事件，到潜在的高增强宇宙射线的加速度。由于在许多尺度上不同过程的复杂相互作用，对磁重新连接的理解是挑战的：从非常小的电子运动的详细物理学到大尺度上的等离子加热和流动产生，再到高能光子和颗粒加速度，可以携带很大一部分可用的等离子能量。该项目的目的是使用机器学习技术来揭示小规模和大尺度上物理过程之间的连接，并开发更好的多尺度磁性重新连接模型。这样一来，它将有助于NSF的“宇宙中的窗户：多门生天体物理学的时代”的目标。 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.这通常被称为找到良好的“封闭”的问题。也就是说，捕获基本过程的一组减少的方程组在未解决的尺度上随着分解量的函数而发生，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来利用实验和高保真模拟的越来越多的数据，以加速所需的减少物理模型的发展。该项目的目的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，以从第一原理等离子体模拟的数据中提取可解释且可推广的减少模型。具体而言，该项目旨在为磁重新连接构建更好的动力学封闭。通过这种基本血浆过程得出更好的颗粒注射和加速模型；并利用这种理解来加速多尺度等离子体算法的发展。虽然直接的重点将放在磁重新连接的问题上，但将开发的工具通常适用于等离子体物理的其他领域，并且更广泛地适用于多体现象。这些多尺度模型的开发可以在等离子体科学的不同领域（从融合到太空和天体物理等离子体）之间产生重大影响。该奖项反映了NSF的法定任务，并通过评估该基金会的知识分子优点和更广泛的影响来审查标准。