jbampton / simulai

A toolkit with data-driven pipelines for physics-informed machine learning.

A Python package with data-driven pipelines for physics-informed machine learning.

The SimulAI toolkit provides easy access to state-of-the-art models and algorithms for physics-informed machine learning. Currently, it includes the following methods described in the literature:

• Physics-Informed Neural Networks (PINNs)
• Deep Operator Networks (DeepONets)
• Variational Encoder-Decoders (VED)
• Operator Inference (OpInf)
• Koopman Autoencoders (experimental)
• Echo State Networks (experimental GPU support)

In addition to the methods above, many more techniques for model reduction and regularization are included in SimulAI. See documentation.

Installing

Python version requirements: 3.8 or 3.9

Using pip

$ pip install simulai-toolkit

Contributing code to SimulAI

See CONTRIBUTING.

Using MPI

Some methods implemented on SimulAI support multiprocessing with MPI.

In order to use it, you will need a valid MPI distribution, e.g. MPICH, OpenMPI. As an example, you can use conda to install MPICH as follows:

$ conda install -c conda-forge mpich gcc

Issues with macOS

If you have problems installing gcc using the command above, we recommend you to install it using Homebrew.

Documentation

Please, refer to the SimulAI API documentation before using the toolkit.

Examples

Additionally, you can refer to examples in the respective folder.

License

This software is licensed under Apache 2.0. See LICENSE.

How to cite SimulAI in your publications

If you find SimulAI to be useful, please consider citing it in your published work:

@misc{simulai,
  author = {IBM},
  title = {SimulAI Toolkit},
  subtitle = {A Python package with data-driven pipelines for physics-informed machine learning},
  note = "https://github.com/IBM/simulai",
  doi = {10.5281/zenodo.7351516},
  year = {2022},
}

References

Jaeger, H., Haas, H. (2004). "Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication," Science, 304 (5667): 78–80. DOI:`10.1126/science.1091277 <https://doi.org/10.1126/science.1091277>`_.

Lu, L., Jin, P., Pang, G., Zhang, Z., Karniadakis, G. E. (2021). "Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators," Nature Machine Intelligence, 3 (1): 218–229. ISSN: 2522-5839. DOI:`10.1038/s42256-021-00302-5 <https://doi.org/10.1038/s42256-021-00302-5>`_.

Eivazi, H., Le Clainche, S., Hoyas, S., Vinuesa, R. (2022) "Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flows" Expert Systems with Applications, 202. ISSN: 0957-4174. DOI:`10.1016/j.eswa.2022.117038 <https://doi.org/10.1016/j.eswa.2022.117038>`_.

Raissi, M., Perdikaris, P., Karniadakis, G. E. (2019). "Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations," Journal of Computational Physics, 378 (1): 686-707. ISSN: 0021-9991. DOI:`10.1016/j.jcp.2018.10.045 <https://doi.org/10.1016/j.jcp.2018.10.045>`_.

Lusch, B., Kutz, J. N., Brunton, S.L. (2018). "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, 9: 4950. ISSN: 2041-1723. DOI:`10.1038/s41467-018-07210-0 <https://doi.org/10.1038/s41467-018-07210-0>`_.

McQuarrie, S., Huang, C. and Willcox, K. (2021). "Data-driven reduced-order models via regularized operator inference for a single-injector combustion process," Journal of the Royal Society of New Zealand, 51(2): 194-211. ISSN: 0303-6758. DOI:`10.1080/03036758.2020.1863237 <https://doi.org/10.1080/03036758.2020.1863237>`_.