Bibliography
G Berkooz, P Holmes, and J L Lumley. The proper orthogonal decomposition in the analysis of turbulent flows. Annual Review of Fluid Mechanics, 25(1):539–575, jan 1993. doi:10.1146/annurev.fl.25.010193.002543.
Steven L Brunton and J Nathan Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, USA, 1st edition, 2019. ISBN 1108422098. URL: http://www.databookuw.com.
Antonio Cammi, Stefano Riva, Carolina Introini, Lorenzo Loi, and Enrico Padovani. Data-Driven Model Order Reduction for Sensor Positioning and Indirect Reconstruction with Noisy Data: Application to a Circulating Fuel Reactor. submitted to Nuclear Engineering and Design, :, 2024. URL:, doi:.
D. Degen, K. Veroy, and F. Wellmann. How Uncertainty Quantification and Reduced Order Modeling Change our Model Understanding. In AGU Fall Meeting Abstracts, volume 2020, T015–0002. December 2020. URL: https://ui.adsabs.harvard.edu/abs/2020AGUFMT015.0002D.
Denise Degen, Karen Veroy, and Florian Wellmann. Certified reduced basis method in geosciences. Computational Geosciences, 24:1–19, 02 2020. doi:10.1007/s10596-019-09916-6.
Denise Degen, Karen Veroy, and Florian Wellmann. Uncertainty quantification for basin-scale geothermal conduction models. Scientific Reports, 12:, 03 2022. doi:10.1038/s41598-022-08017-2.
Theron Guo, Ondřej Rokoš, and Karen Veroy. Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method. Computer Methods in Applied Mechanics and Engineering, 384:113924, 2021. doi:https://doi.org/10.1016/j.cma.2021.113924.
Jan S Hesthaven, Gianluigi Rozza, and Benjamin Stamm. Certified Reduced Basis Methods for Parametrized Partial Differential Equations. Springer International Publishing, 2016. doi:10.1007/978-3-319-22470-1.
Carolina Introini, Stefano Riva, Stefano Lorenzi, Simone Cavalleri, and Antonio Cammi. Non-intrusive system state reconstruction from indirect measurements: A novel approach based on Hybrid Data Assimilation methods. Annals of Nuclear Energy, 182:109538, 2023. URL: https://www.sciencedirect.com/science/article/pii/S0306454922005680, doi:https://doi.org/10.1016/j.anucene.2022.109538.
Yvon Maday. Reduced basis method for the rapid and reliable solution of partial differential equations. November 2006. Preprint. URL: https://hal.archives-ouvertes.fr/hal-00112152.
Yvon Maday, Anthony Patera, James Penn, and Masayuki Yano. A parameterized-background data-weak approach to variational data assimilation: formulation, analysis, and application to acoustics. International Journal for Numerical Methods in Engineering, 2014. doi:10.1002/nme.4747.
Yvon Maday and Anthony T Patera. 4 Reduced basis methods. In Peter Benner, Stefano Grivet-Talocia, Alfio Quarteroni, Gianluigi Rozza, Wil Schilders, and Luís Miguel Silveira, editors, Volume 2 Snapshot-Based Methods and Algorithms, pages 139–180. De Gruyter, 2020. doi:doi:10.1515/9783110671490-004.
Alfio Quarteroni, Andrea Manzoni, and Federico Negri. Reduced Basis Methods for Partial Differential Equations. Springer International Publishing, 2016. doi:10.1007/978-3-319-15431-2.
Alfio Quarteroni and Gianluigi Rozza. Reduced Order Methods for Modeling and Computational Reduction. Springer International Publishing, 2014. doi:10.1007/978-3-319-02090-7.
Stefano Riva, Carolina Introini, and Antonio Cammi. Multi-physics model bias correction with data-driven reduced order techniques: application to nuclear case studies. 2024. URL: https://www.sciencedirect.com/science/article/pii/S0307904X24003196, doi:https://doi.org/10.1016/j.apm.2024.06.040.
Kajal Chandra Saha. Double lid driven cavity with different moving wall directions for low reynolds number flow. International Journal of Applied Mathematics and Theoretical Physics, 4(3):67, 2018. doi:10.11648/j.ijamtp.20180403.11.
Marco Tezzele, Nicola Demo, Andrea Mola, and Gianluigi Rozza. An integrated data-driven computational pipeline with model order reduction for industrial and applied mathematics. In Novel Mathematics Inspired by Industrial Challenges, pages 179–200. Springer International Publishing, Cham, 2022. doi:10.1007/978-3-030-96173-2_7.