References

BGK+20

Peter Benner, Pawan Goyal, Boris Kramer, Benjamin Peherstorfer, and Karen Willcox. Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms. Computer Methods in Applied Mechanics and Engineering, 372:113433, 2020. doi:10.1016/j.cma.2020.113433.

BGW15

Peter Benner, Serkan Gugercin, and Karen Willcox. A survey of projection-based model reduction methods for parametric dynamical systems. SIAM Review, 57(4):483–531, 2015. doi:10.1137/130932715.

GMW22

Mengwu Guo, Shane A McQuarrie, and Karen Willcox. Bayesian operator inference for data-driven reduced-order modeling. Computer Methods in Applied Mechanics and Engineering, 402:115336, 2022. doi:10.1016/j.cma.2022.115336.

IK23

Opal Issan and Boris Kramer. Predicting solar wind streams from the inner-heliosphere to earth via shifted operator inference. Journal of Computational Physics, 473:111689, 2023. doi:10.1016/j.jcp.2022.111689.

JMK21

Parikshit Jain, Shane McQuarrie, and Boris Kramer. Performance comparison of data-driven reduced models for a single-injector combustion process. In AIAA Propulsion and Energy 2021 Forum, 3633. 2021. doi:10.2514/6.2021-3633.

KW22

Parisa Khodabakhshi and Karen E. Willcox. Non-intrusive data-driven model reduction for differential algebraic equations derived from lifting transformations. Computer Methods in Applied Mechanics and Engineering, 389:114296, 2022. doi:10.1016/j.cma.2021.114296.

MHW21

Shane A McQuarrie, Cheng Huang, and Karen Willcox. Data-driven reduced-order models via regularised operator inference for a single-injector combustion process. Journal of the Royal Society of New Zealand, 51:194–211, 2021. doi:10.1080/03036758.2020.1863237.

PW16

Benjamin Peherstorfer and Karen Willcox. Data-driven operator inference for nonintrusive projection-based model reduction. Computer Methods in Applied Mechanics and Engineering, 306:196–215, 2016. doi:10.1016/j.cma.2016.03.025.

QKMW19

Elizabeth Qian, Boris Kramer, Alexandre N. Marques, and Karen E. Willcox. Transform & Learn: A data-driven approach to nonlinear model reduction. In AIAA Aviation 2019 Forum. 2019. AIAA Paper 2019-3707. doi:10.2514/6.2019-3707.

QKPW20

Elizabeth Qian, Boris Kramer, Benjamin Peherstorfer, and Karen Willcox. Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems. Physica D: Nonlinear Phenomena, 406:132401, 2020. doi:10.1016/j.physd.2020.132401.

Qia21

Elizabeth Yi Qian. A scientific machine learning approach to learning reduced models for nonlinear partial differential equations. PhD thesis, Massachusetts Institute of Technology, 2021. URL: https://dspace.mit.edu/handle/1721.1/130748.

SKHW20

Renee Swischuk, Boris Kramer, Cheng Huang, and Karen Willcox. Learning physics-based reduced-order models for a single-injector combustion process. AIAA Journal, 58:2658–2672, 2020. doi:10.2514/1.J058943.