simulai-toolkit 0.99.28 on PyPI

SimulAI

An extensible 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)
Transformers
U-Nets

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

Installing

Python version requirements: 3.9 <= python <= 3.11

Using pip

For installing the most recent stable version from PyPI:

pip install simulai-toolkit

For installing from the latest commit sent to GitHub (just for testing and developing purposes):

pip uninstall simulai_toolkit
pip install -U git+https://github.com/IBM/simulai@$(git ls-remote git@github.com:IBM/simulai.git  | head -1 | awk '{print $1;}')#egg=simulai_toolkit

Contributing code to SimulAI

If you are interested in directly contributing to this project, please 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.

Using Tensorboard

Tensorboard is supported for monitoring neural network training tasks. For a tutorial about how to set it see this example.

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 license 2.0. See LICENSE.

Contributing code to SimulAI

If you are interested in directly contributing to this project, please see CONTRIBUTING.

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},
}

or, via Zenodo:

@software{joao_lucas_de_sousa_almeida_2023_7566603,
      author       = {João Lucas de Sousa Almeida and
                      Leonardo Martins and
                      Tarık Kaan Koç},
      title        = {IBM/simulai: 0.99.13},
      month        = jan,
      year         = 2023,
      publisher    = {Zenodo},
      version      = {0.99.25},
      doi          = {10.5281/zenodo.7566603},
      url          = {https://doi.org/10.5281/zenodo.7566603}
    }

Publications

João Lucas de Sousa Almeida, Pedro Roberto Barbosa Rocha, Allan Moreira de Carvalho and Alberto Costa Nogueira Jr. A coupled Variational Encoder-Decoder - DeepONet surrogate model for the Rayleigh-Bénard convection problem. In When Machine Learning meets Dynamical Systems: Theory and Applications, AAAI, 2023.

João Lucas S. Almeida, Arthur C. Pires, Klaus F. V. Cid, and Alberto C. Nogueira Jr. Non-intrusive operator inference for chaotic systems. IEEE Transactions on Artificial Intelligence, pages 1–14, 2022.

Pedro Roberto Barbosa Rocha, Marcos Sebastião de Paula Gomes, Allan Moreira de Carvalho, João Lucas de Sousa Almeida and Alberto Costa Nogueira Jr. Data-driven reduced-order model for atmospheric CO2 dispersion. In AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges, 2022.

Pedro Roberto Barbosa Rocha, João Lucas de Sousa Almeida, Marcos Sebastião de Paula Gomes, Alberto Costa Nogueira, Reduced-order modeling of the two-dimensional Rayleigh–Bénard convection flow through a non-intrusive operator inference, Engineering Applications of Artificial Intelligence, Volume 126, Part B, 2023, 106923, ISSN 0952-1976, https://doi.org/10.1016/j.engappai.2023.106923. (https://www.sciencedirect.com/science/article/pii/S0952197623011077)

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.

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.

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.

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.

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.

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.