Linlin Zhong

Associate Professor

Southeast University

Biography

I am Linlin Zhong, an Associate Professor at the School of Electrical Engineering, Southeast University. My research interests are directed toward the interdisciplinary study of Plasma Physics, Electrical Engineering, and Artificial Intelligence, including AI-Driven Plasma Modeling, Plasma Basic Data Calculation, Reduced Order Modeling and Digital Twin, Eco-friendly Gases in Power System, and Computer Vision and Federated Learning in Power Maintenance.

Welcome any students who have strong interests in Plasma Engineering, High Voltage Engineering, and Artificial Intelligence to visit my lab and start your magical journey 😄.

Interests

Computational Plasma Physics
Plasma Engineering
High Voltage Engineering
AI + Scientific Computing
AI + Power Maintenance

Education

PhD in Plasma Engineering, 2017

Université Toulouse III - Paul Sabatier
PhD in Electrical Engineering, 2017

Xi'an Jiaotong University
BSc in Electrical Engineering, 2012

Xi'an Jiaotong University

Recent News

More News

Dec. 22 to Dec. 29, 2022: I attended Frontiers in Mathematical Science held in Tsinghua Sanya International Mathematics Forum (TSIMF) and hosted by the world-famous mathematician Prof. Shing-Tung Yau. In this conference, I was invited to give a talk entitled Physics-Informed Low-Temperature Plasma Simulation and Its Acceleration Technology in a session of Applied Mathematics.
Oct. 12, 2022: I attended Online Seminar on Calculation, Verification and Application of Electron-impact Cross Sections hosted by the Key Laboratory of Plasma Dynamics. In this conference, I was invited to give a talk entitled Calculation and Basic Database Construction of Molecular Ionization Cross Sections.
Aug. 26 to Aug. 28, 2022: I attended 2022 National Conference on High Voltage and Discharge Plasmas held in Hefei where I gave a presentation on the meta-learning-based plasma simulation. Because of this presentation, I was honored an Outstanding Oral Report Award.
Aug. 16, 2022: We published a paper on low-temperature plasma simulation based on physics-informed neural networks (PINNs). In this work we proposed two general AI-driven frameworks for low-temperature plasma simulation: Coefficient-Subnet Physics-Informed Neural Network (CS-PINN) and Runge–Kutta Physics-Informed Neural Network (RK-PINN). Based on these two frameworks, we demonstrated preliminary applications in four cases covering plasma kinetic and fluid modeling. The Editors felt that this article is noteworthy, and have chosen it to be promoted as a Featured Article.
This paper is now available on Physics of Fluids.
Jul. 31 to Aug. 05, 2022: I attended The 9th International Congress of Chinese Mathematicians (ICCM2022). In this conference, I was invited by the round table forum with theme of Mathematics + Industry to give a talk entitled Application of AI Technology in Low-Temperature Plasma Simulation. Prof. Shing-Tung Yau, the world-famous mathematician, gave a opening speaking in this thematic forum.
May 27 to May 29, 2022: I attended 2022 IEEE 5th International Electrical and Energy Conference (CIEEC). In this conference, I hosted a session of Plasma Science Technology and Applications, and was invited to give a talk entitled Accelerate Plasma Simulation by Combining Deep Neural Networks and Runge-Kutta Formalism.
May 11, 2022: We published a paper in Chinese on power tower anomaly detection from Unmanned Aerial Vehicle (UAV) inspection images based on improved generative adversarial network (GAN). In this work we proposed Squeeze-and-Excitation improved fast unsupervised anomaly detection with generative adversarial network (SE-f-AnoGAN) for anomaly detection from UAV power tower inspection images. The experimental results show that the accuracy rate of overall samples is 95.74% and the recall rates of positive and negative samples reach 96.05% and 95.36% respectively.
This paper is now available on Transactions of China Electrotechnical Society.
Feb. 07, 2022: I attended again Physics informed AI in Plasma Science (PiAI) Seminar hosted by Prof. Satoshi Hamaguchi in Osaka University. In this seminar, I was invited to give an online talk entitled Runge-Kutta Physics Informed Neural Network (RK-PINN) for solving plasma PDEs with transient terms.

Selected Research Projects

Plasma Basic Data

To calculate basic data of various plasmas, including interaction potential, collision integrals, cross sections, particle compositions, thermodynamic properties, transport coefficients, diffusion coefficients, radiation coefficients, et al.

Thermal Plasma Modeling

To perform magnetohydrodynamic (MHD) simulation of thermal plasmas e.g. high-pressure arc plasma in circuit breakers using finite volume method (FVM)

Eco-friendly Gases

To evaluate the insulating and arc quenching ability of eco-friendly SF₆ replacements

AI Plasma

Deep learning is a powerful non-linear mapping tool which can express very complex non-linear relationships. We are exploring deep-learning-driven methods for solving the governing equations in plasma modeling by constructing deep neural networks to surrogate the solution of plasma models. This could bring us new and prospective numerical tools for plasma modeling.

AI in Power Maintenance

To apply AI technology in assisting intelligent maintenance of power system, e.g. to predict condition of power apparatus or recognize abnormal objects in transmission lines from the videos captured by unmanned aerial vehicles (UAV)

Selected Publications

More Publications

Linlin Zhong, Bingyu Wu, Yifan Wang. Low-temperature plasma simulation based on physics-informed neural networks: frameworks and preliminary applications. Physics of Fluids, 2022.