Machine learning for advancing low-temperature plasma modeling and simulation

• URL: http://arxiv.org/abs/2307.00131v2
• Date: Thu, 14 Dec 2023 21:21:46 GMT
• Title: Machine learning for advancing low-temperature plasma modeling and simulation
• Authors: Jan Trieschmann, Luca Vialetto, Tobias Gergs
• Abstract summary: We review the state-of-the-art focusing on approaches to low-temperature plasma modeling and simulation. We provide a perspective of potential advances to plasma science and technology.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be carefully assessed in general, many aspects of plasma modeling and simulation have benefited substantially from recent developments within the field of machine learning and data-driven modeling. In this survey, we approach two main objectives: (a) We review the state-of-the-art focusing on approaches to low-temperature plasma modeling and simulation. By dividing our survey into plasma physics, plasma chemistry, plasma-surface interactions, and plasma process control, we aim to extensively discuss relevant examples from literature. (b) We provide a perspective of potential advances to plasma science and technology. We specifically elaborate on advances possibly enabled by adaptation from other scientific disciplines. We argue that not only the known unknowns, but also unknown unknowns may be discovered due to the inherent propensity of data-driven methods to spotlight hidden patterns in data.

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