@Article{EAJAM-14-636,
author = {Kwon , HeejaeKim , EunsuhCho , SunghaKwon , Deuk-ChulChoe , Hee-Hwan and Choi , Minseok},
title = {Plasma-Simulation Physics Informed Neural Networks (PS-PINNs) for Global Discharge Models},
journal = {East Asian Journal on Applied Mathematics},
year = {2024},
volume = {14},
number = {3},
pages = {636--656},
abstract = {<p style="text-align: justify;">We consider the global discharge model in plasma simulations using physics-informed neural networks (PINNs). Our method, named Plasma-Simulation PINNs (PS-PINNs), effectively addresses the inherent stiffness and multiphysics aspects of the global
model. Logarithmically equidistant points are employed to capture the steep behaviour
in state variables during early stages. This distribution, while typical for handling stiffness in standard numerical methods, can hinder neural network (NN) training. To overcome this, we introduce a pre-processing layer featuring logarithmic transformation and
standardization, significantly improving neural network training efficiency. In addition,
our model addresses the complex interactions between multiple species common in the
plasma problem, resulting in numerous physics loss terms. It is important to balance
among various loss terms during training. To this end, we employ a self-adaptive loss
balanced method to adaptively choose the weights, enhancing training robustness and
effectiveness. The effectiveness of the proposed framework is demonstrated through
several examples, including the forward and inverse problems in the chlorine global
discharge model, and parameter dependency analysis.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.2023-313.170324},
url = {http://global-sci.org/intro/article_detail/eajam/23164.html}
}