Reduced-order Modeling via Operator Inference for 2D Combustion

This repository is an extensive example of Operator Inference, a data-driven procedure for reduced-order modeling, applied to a two-dimensional single-injector combustion problem. The following branches are the source code for publications that use this example (see References below).

• bayes is the source code of the paper Bayesian operator inference for data-driven reduced-order modeling by Guo, McQuarrie, and Willcox.
• aiaape2021 is the source code for the paper Performance comparison of data-driven reduced models for a single-injector combustion process by Jain, McQuarrie, and Kramer.
• jrsnz2021 is the source code for the paper Data-driven reduced-order models via regularised operator inference for a single-injector combustion process by McQuarrie, Huang, and Willcox.

The code can also replicate the results of the paper Learning physics-based reduced-order models for a single-injector combustion process by Swischuk, Kramer, Huang, and Willcox.

See the Wiki for details on the problem statement, instructions for using this repository, and visual results.

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Contributors: Shane McQuarrie, Renee Swischuk, Parikshit Jain, Boris Kramer, Mengwu Guo, Karen Willcox

References

• Guo, M, McQuarrie, S. A., and Willcox, K. E., Bayesian operator inference for data-driven reduced-order modeling. arXiv preprint 2204.10829, 2022. (Download)

  BibTeX

  @article{GMW2022BayesOpInf,
  author = {Mengwu Guo and Shane A. McQuarrie and Karen E. Willcox},
  title = {{B}ayesian operator inference for data-driven reduced-order modeling},
  journal = {arXiv preprint arXiv:2204.10829},
  year = {2022},
  }

• Jain, P., McQuarrie, S. A., and Kramer, B., Performance comparison of data-driven reduced models for a single-injector combustion process. AIAA Propulsion and Energy Forum and Exposition, 2021. Paper AIAA-2021-3633. (Download)

  BibTeX

  @inproceedings{jain2021performance,
  title = {Performance comparison of data-driven reduced models for a single-injector combustion process},
  author = {Parikshit Jain and Shane A. McQuarrie and Boris Kramer},
  booktitle = {AIAA Propulsion and Energy 2021 Forum},
  year = {2021},
  address = {Virtual Event},
  note = {Paper AIAA-2021-3633},
  }

• McQuarrie, S. A., Huang, C., and Willcox, K. E., Data-driven reduced-order models via regularised operator inference for a single-injector combustion process. Journal of the Royal Society of New Zealand, Vol. 51:2, pp. 194-211, 2021. (Download)

  BibTeX

  @article{MHW2021regOpInfCombustion,
  author = {Shane A. McQuarrie and Cheng Huang and Karen E. Willcox},
  title = {Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process},
  journal = {Journal of the Royal Society of New Zealand},
  volume = {51},
  number = {2},
  pages = {194--211},
  year = {2021},
  publisher = {Taylor & Francis},
  }

• Swischuk, R., Kramer, B., Huang, C., and Willcox, K., Learning physics-based reduced-order models for a single-injector combustion process. AIAA Journal, Vol. 58:6, pp. 2658-2672, 2020. Also in Proceedings of 2020 AIAA SciTech Forum & Exhibition, Orlando FL, January, 2020. Also Oden Institute Report 19-13. (Download)

  BibTeX

  @article{SKHW2020romCombustion,
  title = {Learning physics-based reduced-order models for a single-injector combustion process},
  author = {Renee Swischuk and Boris Kramer and Cheng Huang and Karen Willcox},
  journal = {AIAA Journal},
  volume = {58},
  number = {6},
  pages = {2658--2672},
  year = {2020},
  publisher = {American Institute of Aeronautics and Astronautics}
  }

• Huang, C. (2020). [Updated] 2D Benchmark Reacting Flow Dataset for Reduced Order Modeling Exploration [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/nj7w-j319.

• ROM Operator Inference Python 3 package (pypi)