References

• Guo, M., McQuarrie, S. A., and Willcox, K., Bayesian operator inference for data-driven reduced-order modeling. Computer Methods in Applied Mechanics and Engineering, Vol. 402, pp. 115336, 2022.

  BibTeX

  @article{guo2022bayesopinf,
  title = {Bayesian operator inference for data-driven reduced-order modeling},
  author = {Guo, M. and McQuarrie, S. and Willcox, K.},
  journal = {Computer Methods in Applied Mechanics and Engineering},
  year = {2022},
  volume = {402},
  pages = {115336},
  doi = {10.1016/j.cma.2022.115336},
  }

• Huang, C. (2020). [Updated] 2D Benchmark Reacting Flow Dataset for Reduced Order Modeling Exploration [Data set]. University of Michigan - Deep Blue.

• Jain, P., McQuarrie, S. A., and Kramer, B., Performance comparison of data-driven reduced models for a single-injector combustion process. In AIAA Propulsion and Energy 2021 Forum, Virtual, 2021.

  BibTeX

  @inproceedings{jain2021performance,
  title = {Performance comparison of data-driven reduced models for a single-injector combustion process},
  author = {Jain, Parikshit and McQuarrie, Shane A. and Kramer, Boris},
  booktitle = {AIAA Propulsion and Energy 2021 Forum},
  year = {2021},
  address = {Virtual event},
  note = {AIAA Paper 2021-3633},
  doi = {10.2514/6.2021-3633},
  }

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

  BibTeX

  @article{mcquarrie2021combustion,
  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},
  doi = {10.1080/03036758.2020.1863237},
  }

• 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.

  BibTeX

  @article{swischuk2020combustion,
  title = {Learning physics-based reduced-order models for a single-injector combustion process},
  author = {Swischuk, Renee and Kramer, Boris and Huang, Cheng and Willcox, Karen},
  journal = {AIAA Journal},
  volume = {58},
  number = {6},
  pages = {2658--2672},
  year = {2020},
  publisher = {American Institute of Aeronautics and Astronautics},
  doi = {10.2514/1.J058943},
  }

• opinf Python package (pypi)