# pinn-sampling

**Repository Path**: happtain/pinn-sampling

## Basic Information

- **Project Name**: pinn-sampling
- **Description**: copy from https://github.com/lu-group/pinn-sampling.git
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-12-04
- **Last Updated**: 2025-12-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://doi.org/10.1016/j.cma.2022.115671).

## Code

- [Diffusion equation](src/diffusion)
- [Burgers’ equation](src/burgers)
- [Allen–Cahn equation](src/allen_cahn)
- [Wave equation](src/wave)
- [Diffusion–reaction equation (inverse problem)](src/diffusion_reaction_inverse)
- [Korteweg–de Vries equation (inverse problem)](src/korteweg_de_vries_inverse)

## 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.