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Found 5 papers, 2 papers with code
More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning
The experimental results demonstrate that JGRM outperforms existing methods in both road segment representation and trajectory representation tasks.
Proposing an intelligent mesh smoothing method with graph neural networks
With a lightweight model, GMSNet can effectively smoothing mesh nodes with varying degrees and remain unaffected by the order of input data.
Auxiliary-Tasks Learning for Physics-Informed Neural Network-Based Partial Differential Equations Solving
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.
ST-PINN: A Self-Training Physics-Informed Neural Network for Partial Differential Equations
To address the issue of low accuracy and convergence problems of existing PINNs, we propose a self-training physics-informed neural network, ST-PINN.
An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks
To improve the prediction accuracy of the neural network, we also introduce a novel auxiliary line strategy and an efficient network model during meshing.
