Abstract
Abstract
Physics-informed neural networks (PINNs) have a wide range of applications as an alternative to traditional numerical methods in plasma simulation. However, in some specific cases of PINN-based modeling, a well-trained PINN may require tens of thousands of optimizing iterations during training stage for complex modeling and huge neural networks, which is sometimes very time-consuming. In this work, we propose a meta-learning method, namely Meta-PINN, to reduce the training time of PINN-based 1D arc simulation. In Meta-PINN, the meta network is first trained by a two-loop optimization on various training tasks of plasma modeling, and then used to initialize the PINN-based network for new tasks. We demonstrate the power of Meta-PINN by four cases corresponding to 1D arc models at different boundary temperatures, arc radii, arc pressures, and gas mixtures. We found that a well-trained meta network can produce good initial weights for PINN-based arc models even at conditions slightly outside of training range. The speed-up in terms of relative L2 error by Meta-PINN ranges from 1.1× to 6.9× in the cases we studied. The results indicate that Meta-PINN is an effective method for accelerating the PINN-based 1D arc simulation.
Funder
Fundamental Research Funds for the Central Universities
National Natural Science Foundation of China
Zhishan Young Scholar Project of Southeast University
Young Scientific and Technical Talents Promotion Project of Jiangsu Association for Science and Technology
Subject
Surfaces, Coatings and Films,Acoustics and Ultrasonics,Condensed Matter Physics,Electronic, Optical and Magnetic Materials
Cited by
2 articles.
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