References
[1] Liu P, Wu Q, Ren X, Wang Y, Ni D. A deep-learning-based surrogate modeling method with application to plasma processing. Chemical Engineering Research and Design. 2024;211:299–317. doi:10.1016/j.cherd.2024.09.031.
[2] Yin B, Zhu Y, Chen X, Wu Y. The capability of a deep learning based ODE solution for low temperature plasma chemistry. Physics of Plasmas. 2024;31(6):063504. doi:10.1063/5.0208790.
[3] Arellano FJ, Kusaba M, Wu S, Yoshida R, Donkó Z, Hartmann P, et al. Machine learning-based prediction of the electron energy distribution function and electron density of argon plasma from the optical emission spectra. Journal of Vacuum Science & Technology A. 2024;42(5):053001. doi:10.1116/6.0003731.
[4] Zhong L, Wu B, Wu Q. Numerical solution of electron Boltzmann equation in gas discharge plasmas based on meta learning. Transactions of China Electrotechnical Society. 2024;39(11):3457–3466. doi:10.19595/j.cnki.1000-6753.tces.230573.
[5] Muccignat DL, Boyle GG, Garland NA, Stokes PW, White RD. An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients. Machine Learning: Science and Technology. 2024;5(1):015047. doi:10.1088/2632-2153/ad2fed.
[6] Trieschmann J, Vialetto L, Gergs T. Review: Machine learning for advancing low-temperature plasma modeling and simulation. Journal of Micro/Nanopatterning, Materials, and Metrology. 2023;22(4):041504. doi:10.1117/1.JMM.22.4.041504.
[7] Kajita S, Nishijima D. Helium line emission spectroscopy to measure plasma parameters using modeling and machine learning in low-temperature plasmas. Journal of Physics D: Applied Physics. 2024;57(42):423003. doi:10.1088/1361-6463/ad6007.
[8] Rafiq T, Kritz AH, Weiland J, Pankin AY, Luo L. Physics basis of Multi-Mode anomalous transport module. Physics of Plasmas. 2013;20(3):032506. doi:10.1063/1.4796043.
[9] Morosohk S, Pajares A, Rafiq T, Schuster E. Neural network model of the multi-mode anomalous transport module for accelerated transport simulations. Nuclear Fusion. 2021;61(10):106040. doi:10.1088/1741-4326/ac20d5.
[10] Meneghini O, Smith S, Snyder P, Staebler G, Candy J, Belli E, et al. Self-consistent core-pedestal transport simulations with neural network accelerated models. Nuclear Fusion. 2017;57(8):086034. doi:10.1088/1741-4326/aa6fd6.
[11] Leard B, Wang Z, Morosohk S, Rafiq T, Schuster E. Fast Neural-Network Surrogate Model of the Updated Multi-Mode Anoma-lous Transport Module for NSTX-U. IEEE Transactions on Plasma Science. 2024;52(9):4126–4132. doi:10.1109/TPS.2024.3421593.
[12] Pankin A, Breslau J, Gorelenkova M, Andre R, Grierson B, Sachdev J, et al. TRANSP integrated modeling code for interpretive and predictive analysis of tokamak plasmas. arXiv preprint. 2024. arXiv:2406.07781. https://arxiv.org/abs/2406.07781.
[13] Boyer M, Kaye S, Erickson K. Real-time capable modeling of neutral beam injection on NSTX-U using neural networks. Nuclear Fusion. 2019;59(5):056008. doi:10.1088/1741-4326/ab09b2.
[14] Wang Z, Morosohk S, Rafiq T, Schuster E, Boyer M, Choi W. Neural network model of neutral beam injection in the EAST tokamak to enable fast transport simulations. Fusion Engineering and Design. 2023;191:113514. doi:10.1016/j.fusengdes.2023.113514.
[15] Leyh H, Loffhagen D, Winkler R. A new multi-term solution technique for the electron Boltzmann equation of weakly ionized steady-state plasmas. Computer Physics Communications. 1998;113(1):33–48. doi:10.1016/s0010-4655(98)00062-9.
[16] Siffa IC, Loffhagen D, Becker MM, Trieschmann J. A physics-informed neural network approach to solve the spatially inhomogeneous electron Boltzmann equation. arXiv preprint. 2026. arXiv:2605.04307. https://arxiv.org/abs/2605.04307.
[17] Haghighi A, Shadloo MS, Maleki A, Abdollahzadeh Jamalabadi MY. Using Committee Neural Network for Prediction of Pressure Drop in Two-phase Microchannels. Applied Sciences. 2020;10(15):5384. doi:10.3390/app10155384.
[18] Abdollahzadeh Jamalabadi MY. Wisdom of Neural Committees: A Synthetic Benchmark Study on Robust Ensemble Prediction of Two-Phase Flow Pressure Drop from Basic Fluid Properties. International Journal of Statistics and Data Science. 2026;2(1):30–43. doi:10.26855/ijsds.2026.06.003.
[19] Abdollahzadeh Jamalabadi MY. Analytical and Numerical Simulation of a Microwave-Sustained Argon Plasma Torch at Moderate Pressures. Journal of High-Frequency Communication Technologies. 2026;4(02):497–519. doi:10.58399/KHTZ5004.