Improving Interpretable Machine Learning for Plasmas: Towards Physical Insight, Data-Driven Models, and Optimal Sensing

改进等离子体的可解释机器学习：迈向物理洞察、数据驱动模型和最佳传感

• 批准号: 2329765
• 负责人: Christopher Hansen
• 金额: $ 56.99万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2329765&HistoricalAwards=false
• 关键词: Improving; Interpretable; Machine; Learning; Plasmas; Improving; Interpretable; Machine; Learning; Plasmas

Magnetized plasmas, a combination of superheated gas and magnetic fields, are pervasive in our universe and are responsible for some of the grandest natural phenomena, such as the aurora. Plasmas are also extensively studied for engineering and industrial applications, such as space propulsion and development of future fusion energy reactors. This project aims to improve our ability to understand and predict the behavior of magnetized plasmas using simplified models that are both fast and easy to use. In particular, this investigation will explore methods that combine machine learning techniques that are revolutionizing many fields, like self-driving cars, with the known physical laws that govern magnetized plasmas - seeking to leverage the best aspects of each individual approach.This project will advance data-driven modeling approaches such as machine learning by utilizing physics-informed constraints for magnetized plasmas in three ways: 1) Several emerging data decomposition methods will be applied to numerical simulations of magnetized plasmas for the first time and assessed for these systems; 2) Data-driven nonlinear models based on these decompositions will be tested for modeling magnetized plasmas with significantly increased speed compared to classical approaches; 3) Methods to optimize the placement of sensors to diagnose magnetized plasmas will be evaluated to improve the value of measurements used to both observe plasmas and as the source of information to build data-driven models. Together these three studies will advance the effectiveness of low-dimensional, nonlinear, and interpretable data-driven methods for achieving new physical insight, improved prediction, and robust control of multi-scale magnetized 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.

磁化等离子体（过热气体和磁场的组合）在我们的宇宙中普遍存在，并负责某些最宏伟的自然现象，例如Aurora。 还广泛研究了用于工程和工业应用的血浆，例如太空推进和未来融合能量反应器的开发。 该项目旨在使用既快速又易于使用的简化模型来提高我们理解和预测磁化等离子体的行为的能力。 In particular, this investigation will explore methods that combine machine learning techniques that are revolutionizing many fields, like self-driving cars, with the known physical laws that govern magnetized plasmas - seeking to leverage the best aspects of each individual approach.This project will advance data-driven modeling approaches such as machine learning by utilizing physics-informed constraints for magnetized plasmas in three ways: 1) Several emerging data decomposition methods will be applied首次对磁化等离子体进行数值模拟，并对这些系统进行评估； 2）与经典方法相比，基于这些分解的数据驱动的非线性模型将测试以速度显着提高的磁化等离子体； 3）将评估优化传感器放置以诊断磁化等离子体的方法，以提高用于观察等离子体的测量值和作为构建数据驱动模型的信息来源。 Together these three studies will advance the effectiveness of low-dimensional, nonlinear, and interpretable data-driven methods for achieving new physical insight, improved prediction, and robust control of multi-scale magnetized 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.