This repository is an extensive example of the non-intrusive, data-driven Operator Inference procedure for reduced-order modeling applied to a two-dimensional combustion problem. It is the source code for [1] (see the jrsnz2021 branch) and can be used to reproduce the results of [2].

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

Contributors: Shane A. McQuarrie, Renee Swischuk, Parikshit Jain, Boris Kramer, Karen Willcox

• [1] McQuarrie, S. A., Huang, C., and Willcox, K., Data-driven reduced-order models via regularized operator inference for a single-injector combustion process. arXiv:2008.02862. (Download)

  BibTeX

  @article{MHW2021regOpInfCombustion,
  title   = {Data-driven reduced-order models via regularized operator inference for a single-injector combustion process},
  author  = {McQuarrie, S. A. and Huang, C. and Willcox, K.},
  journal = {arXiv preprint arXiv:2008.02862},
  year    = {2021}
  }

• [2] 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    = {Swischuk, R. and Kramer, B. and Huang, C. and Willcox, K.},
  journal   = {AIAA Journal},
  volume    = {58},
  number    = {6},
  pages     = {2658--2672},
  year      = {2020},
  publisher = {American Institute of Aeronautics and Astronautics}
  }

• [3] 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.

• [4] ROM Operator Inference Python 3 package (pypi)