no code implementations • 24 Sep 2023 • Zhichao Wang, Xinhai Chen, Junjun Yan, Jie Liu
With a lightweight model, GMSNet can effectively smoothing mesh nodes with varying degrees and remain unaffected by the order of input data.
1 code implementation • 12 Jul 2023 • Junjun Yan, Xinhai Chen, Zhichao Wang, Enqiang Zhou, Jie Liu
To alleviate these issues, we proposed auxiliary-task learning-based physics-informed neural networks (ATL-PINNs), which provide four different auxiliary-task learning modes and investigate their performance compared with original PINNs.
1 code implementation • 15 Jun 2023 • Junjun Yan, Xinhai Chen, Zhichao Wang, Enqiang Zhoui, Jie Liu
To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN.
no code implementations • 18 Oct 2022 • Xinhai Chen, Jie Liu, Junjun Yan, Zhichao Wang, Chunye Gong
To improve the prediction accuracy of the neural network, we also introduce a novel auxiliary line strategy and an efficient network model during meshing.