ROM-OpInf-Combustion-2D
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Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" by S. A. McQuarrie, C. Huang, and K. E. Willcox.
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).
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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.
Contributors: Shane McQuarrie, Renee Swischuk, Parikshit Jain, Boris Kramer, Mengwu Guo, Karen Willcox
References
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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}, }
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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}, }
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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}, }
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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} }
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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.