Data-driven sparse modeling of oscillations in plasma space propulsion

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Borja Bayón-Buján, Mario Merino
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引用次数: 0

Abstract

An algorithm to obtain data-driven models of oscillatory phenomena in plasma space propulsion systems is presented, based on sparse regression (SINDy) and Pareto front analysis. The algorithm can incorporate physical constraints, use data bootstrapping for additional robustness, and fine-tuning to different metrics. Standard, weak and integral SINDy formulations are discussed and compared. The scheme is benchmarked for the case of breathing-mode oscillations in Hall effect thrusters, using particle-in-cell/fluid simulation data. Models of varying complexity are obtained for the average plasma properties, and shown to have a clear physical interpretability and agreement with existing 0D models in the literature. Lastly, the algorithm applied is also shown to enable the identification of physical subdomains with qualitatively different plasma dynamics, providing valuable information for more advanced modeling approaches.
等离子体空间推进器振荡的数据驱动稀疏建模
基于稀疏回归(SINDy)和帕累托前沿分析,介绍了一种获得等离子空间推进系统振荡现象数据驱动模型的算法。该算法可以结合物理约束条件,使用数据引导以获得额外的鲁棒性,并根据不同的指标进行微调。对标准、弱和积分 SINDy 公式进行了讨论和比较。针对霍尔效应推进器中的呼吸模式振荡情况,利用舱内粒子/流体模拟数据对该方案进行了基准测试。针对等离子体的平均特性获得了不同复杂度的模型,结果表明这些模型与文献中现有的 0D 模型具有明确的物理可解释性和一致性。最后,所应用的算法还证明能够识别具有质的不同等离子体动力学的物理子域,为更先进的建模方法提供有价值的信息。
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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