46 citations · 125 across the 4 of their papers we have counts for
4 papers
NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs
Yifan Wang, Linlin Zhong
Physics-informed neural network (PINN) has been a prevalent framework for solving PDEs since proposed. By incorporating the physical information into the neural network through los…
Accelerating physics-informed neural network based 1D arc simulation by meta learning
Linlin Zhong, Bingyu Wu, Yifan Wang
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…
Low-temperature plasma simulation based on physics-informed neural networks: frameworks and preliminary applications
Linlin Zhong, Bingyu Wu, Yifan Wang
Plasma simulation is an important and sometimes only approach to investigating plasma behavior. In this work, we propose two general AI-driven frameworks for low-temperature plasma…
Deep learning for thermal plasma simulation: solving 1-D arc model as an example
Linlin Zhong, Qi Gu, Bingyu Wu
Numerical modelling is an essential approach to understanding the behavior of thermal plasmas in various industrial applications. We propose a deep learning method for solving the…