Data‐driven plasma science: A new perspective on modeling, diagnostics, and applications through machine learning

Abstract

AbstractThis paper comprehensively explores the integration of machine learning (ML) with atmospheric pressure plasma, highlighting its transformative impact in areas, such as modeling, diagnostics, and applications. The paper delves into the application of neural networks and deep learning models in simulating complex plasma dynamics, enhancing prediction accuracy, and reducing computational demands. We also examine the application of ML in plasma diagnostics, including real‐time data analysis and process optimization, demonstrating advancements in monitoring and controlling plasma systems. The article discusses the challenges encountered in this integration process, such as data quality, computational resources, and model interpretability. Finally, we outline future development directions, emphasizing the potential of ML in revolutionizing plasma research, improving operational efficiency, and opening new avenues in plasma technology.