Adaptive control of reaction-diffusion PDEs via neural operator approximated gain kernels and parameter estimators

The source code for the paper titled Adaptive control of reaction-diffusion PDEs via neural operator approximated gain kernels and parameter estimators TODO: Add arXiv link.

Sysetm Requirements

All of the code is written in Python 3 and relies on standard packages such as numpy, Pytorch, Scipy, and the deep learning package DeepXDE. Additionally, all code in this work is nicely formatted in a jupyter-notebook. A basic installation will require the installation of Python, jupyter along with DeepXDE and PyTorch. Please see the import statements in the Jupyter-notebooks to make sure all files are included. Versions for each package is given at the first block of the jupyter-notebook and is included in the requirements.txt file.

Demos

Dataset and Models

All precomputed datasets and models are available here Google Drive

Adaptive Control Simulations

• Please see the jupyter-notebook adaptive.ipynb. All the datasets are available in the drive, but if one ones to make their own data, the generation code is commented out in the notebook. Likewise to compute one's own models, please comment out the load_dict in the notebook.

Cite this work (TODO)

@misc{bhan2024adaptivecontrolreactiondiffusionpdes,
      title={Adaptive control of reaction-diffusion PDEs via neural operator-approximated gain kernels}, 
      author={Luke Bhan and Yuanyuan Shi and Miroslav Krstic},
      year={2024},
      eprint={2407.01745},
      archivePrefix={arXiv},
      primaryClass={eess.SY},
      url={https://arxiv.org/abs/2407.01745}, 
}

Questions

Feel free to leave any questions in the issues of Github or email the author Luke at lbhan@ucsd.edu

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.