Neural Operators for Advection Diffusion PDE Control

The source code for the paper titled Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs: arxiv

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.

Demos

Dataset and Models

All precomputed datasets and models are available here Google Drive

All code is available in Jupyter-notebook and should be very straightforward to run. Datasets do not take long to generate and the model may take about a half hour to 45 minutes.

Cite this work TODO

@misc{krstic2023neural,
    title={Neural Operators of Backstepping Controller and Observer Gain Functions for Reaction-Diffusion PDEs},
    author={Miroslav Krstic and Luke Bhan and Yuanyuan Shi},
    year={2023},
    eprint={2303.10506},
    archivePrefix={arXiv},
    primaryClass={eess.SY}
}

Questions

Feel free to leave any questions in the issues of Github or email the author Luke at [email protected]

License

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