Reliable extrapolation of deep neural operators informed by physics or sparse observations

The data and code for the paper M. Zhu, H. Zhang, A. Jiao, G. E. Karniadakis, & L. Lu. Reliable extrapolation of deep neural operators informed by physics or sparse observations. Computer Methods in Applied Mechanics and Engineering, 412, 116064, 2023.

Datasets

• Diffusion-reaction equation
• Burgers’ equation
• Advection equation
• Poisson equation
• Cavity flow

To get complete datasets, run data_gen.py and data_gen_low_fidelity.py in the data folder for each equation.

Code

• Diffusion-reaction equation
• Burgers’ equation
• Advection equation
• Poisson equation
• Cavity flow

Cite this work

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

@article{zhu2023reliable,
  title   = {Reliable extrapolation of deep neural operators informed by physics or sparse observations},
  author  = {Zhu, Min and Zhang, Handi and Jiao, Anran and Karniadakis, George Em and Lu, Lu},
  journal = {Computer Methods in Applied Mechanics and Engineering},
  volume  = {412},
  pages   = {116064},
  year    = {2023},
  doi     = {https://doi.org/10.1016/j.cma.2023.116064}
}

Questions

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