PINN-sampling: Non-adaptive and residual-based adaptive sampling for PINNs

The data and code for the paper C. Wu, M. Zhu, Q. Tan, Y. Kartha, & L. Lu. A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks. Computer Methods in Applied Mechanics and Engineering, 403, 115671, 2023.

Code

• Diffusion equation
• Burgers’ equation
• Allen–Cahn equation
• Wave equation
• Diffusion–reaction equation (inverse problem)
• Korteweg–de Vries equation (inverse problem)

Cite this work

If you use this data or code for academic research, you are encouraged to cite the following paper:

@article{wu2023comprehensive,
  title   = {A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks},
  author  = {Wu, Chenxi and Zhu, Min and Tan, Qinyang and Kartha, Yadhu and Lu, Lu},
  journal = {Computer Methods in Applied Mechanics and Engineering},
  volume  = {403},
  pages   = {115671},
  year    = {2023},
  doi     = {https://doi.org/10.1016/j.cma.2022.115671}
}

Questions

To get help on how to use the data or code, simply open an issue in the GitHub "Issues" section.