标题/作者	年份
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders R Maulik, B Lusch, P Balaprakash Physics of Fluids 33 (3)	2021
Deep neural networks for nonlinear model order reduction of unsteady flows H Eivazi, H Veisi, MH Naderi, V Esfahanian Physics of Fluids 32 (10)	2020
Time-series learning of latent-space dynamics for reduced-order model closure R Maulik, A Mohan, B Lusch, S Madireddy, P Balaprakash, D Livescu Physica D: Nonlinear Phenomena 405, 132368	2020
On closures for reduced order models—A spectrum of first-principle to machine-learned avenues SE Ahmed, S Pawar, O San, A Rasheed, T Iliescu, BR Noack Physics of Fluids 33 (9)	2021
Reduced-order modeling Z Bai, PM Dewilde, RW Freund Handbook of numerical analysis 13, 825-895	2005
Data-driven recovery of hidden physics in reduced order modeling of fluid flows S Pawar, SE Ahmed, O San, A Rasheed Physics of Fluids 32 (3)	2020
Machine learning for nonintrusive model order reduction of the parametric inviscid transonic flow past an airfoil SA Renganathan, R Maulik, V Rao Physics of Fluids 32 (4)	2020
Promoting global stability in data-driven models of quadratic nonlinear dynamics AA Kaptanoglu, JL Callaham, A Aravkin, CJ Hansen, SL Brunton Physical Review Fluids 6 (9), 094401	2021
Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches AA Kaptanoglu, KD Morgan, CJ Hansen, SL Brunton Physical Review E 104 (1), 015206	2021
A long short-term memory embedding for hybrid uplifted reduced order models SE Ahmed, O San, A Rasheed, T Iliescu Physica D: Nonlinear Phenomena 409, 132471	2020
Data-driven variational multiscale reduced order models C Mou, B Koc, O San, LG Rebholz, T Iliescu Computer Methods in Applied Mechanics and Engineering 373, 113470	2021
Continuous data assimilation reduced order models of fluid flow C Zerfas, LG Rebholz, M Schneier, T Iliescu Computer Methods in Applied Mechanics and Engineering 357, 112596	2019
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence S Pawar, O San, A Rasheed, P Vedula Theoretical and Computational Fluid Dynamics 34, 429-455	2020
Data-driven correction reduced order models for the quasi-geostrophic equations: A numerical investigation C Mou, H Liu, DR Wells, T Iliescu International Journal of Computational Fluid Dynamics 34 (2), 147-159	2020
A dynamic closure modeling framework for model order reduction of geophysical flows SM Rahman, SE Ahmed, O San Physics of Fluids 31 (4)	2019
Sampling and resolution characteristics in reduced order models of shallow water equations: Intrusive vs nonintrusive SE Ahmed, O San, DA Bistrian, IM Navon International Journal for Numerical Methods in Fluids 92 (8), 992-1036	2020
Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning H Gao, JX Wang, MJ Zahr Physica D: Nonlinear Phenomena 412, 132614	2020
On the role of nonlinear correlations in reduced-order modelling JL Callaham, SL Brunton, JC Loiseau Journal of Fluid Mechanics 938, A1	2022
A finite element reduced‐order model based on adaptive mesh refinement and artificial neural networks J Baiges, R Codina, I Castanar, E Castillo International Journal for Numerical Methods in Engineering 121 (4), 588-601	2020
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation R Maulik, T Botsas, N Ramachandra, LR Mason, I Pan Physica D: Nonlinear Phenomena 416, 132797	2021

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