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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.

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Willcox-Research-Group/ROM-OpInf-Combustion-2D

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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).

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.


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

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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.

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