References

• [1] McQuarrie, S., Huang, C., and Willcox, K., Data-driven reduced-order models via regularized operator inference for a single-injector combustion process, in review.

• [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). 2D Benchmark Reacting Flow Dataset for Reduced Order Modeling Exploration [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/jrdr-bj37.

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