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[1] Jan S. Hesthaven,et al. Physics-informed machine learning for reduced-order modeling of nonlinear problems , 2021, J. Comput. Phys..
[2] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[3] Gregory D. Hager,et al. Temporal Convolutional Networks for Action Segmentation and Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[5] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[6] H. Mori. Transport, Collective Motion, and Brownian Motion , 1965 .
[7] Petros,et al. Generalized Stability Theory . Part I : Autonomous Operators , 2022 .
[8] P. Holmes,et al. The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows , 1993 .
[9] Gianmarco Mengaldo,et al. PySPOD: A Python package for Spectral Proper Orthogonal Decomposition (SPOD) , 2021, J. Open Source Softw..
[10] G. Balsamo,et al. Spectral Empirical Orthogonal Function Analysis of Weather and Climate Data , 2019, Monthly Weather Review.
[11] Prasanna Balaprakash,et al. Time-series learning of latent-space dynamics for reduced-order model closure , 2019, Physica D: Nonlinear Phenomena.
[12] R. Zwanzig. Nonlinear generalized Langevin equations , 1973 .
[13] J. Lumley. Stochastic tools in turbulence , 1970 .
[14] A. Föhrenbach,et al. SIMPLE++ , 2000, OR Spectr..
[15] J. Hesthaven,et al. Non-intrusive reduced order modeling of nonlinear problems using neural networks , 2018, J. Comput. Phys..
[16] Gianluigi Rozza,et al. Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flows , 2019, J. Comput. Phys..
[17] L. Sirovich. Turbulence and the dynamics of coherent structures. II. Symmetries and transformations , 1987 .
[18] Sanjiva K. Lele,et al. Modelling of jet noise: a perspective from large-eddy simulations , 2019, Philosophical Transactions of the Royal Society A.
[19] K. Huth. Transport , 2015, Canadian Medical Association Journal.
[20] P. Alam. ‘A’ , 2021, Composites Engineering: An A–Z Guide.
[21] Alex Sherstinsky,et al. Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network , 2018, Physica D: Nonlinear Phenomena.
[22] S. Lele,et al. Broadband reconstruction of inhomogeneous turbulence using spectral proper orthogonal decomposition and Gabor modes , 2020, Journal of Fluid Mechanics.
[23] Prashant D. Sardeshmukh,et al. Reconciling Non-Gaussian Climate Statistics with Linear Dynamics , 2009 .
[24] Koji Fukagata,et al. CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers , 2020, Fluid Dynamics Research.
[25] O. Schmidt,et al. Frequency–time analysis, low-rank reconstruction and denoising of turbulent flows using SPOD , 2020, Journal of Fluid Mechanics.
[26] B. Hamzi,et al. Simple, low-cost and accurate data-driven geophysical forecasting with learned kernels , 2021, Proceedings of the Royal Society A.
[27] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[28] G. North,et al. Empirical Orthogonal Functions: The Medium is the Message , 2009 .
[29] Qian Wang,et al. Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism , 2020, J. Comput. Phys..
[30] T. Colonius,et al. Spectral proper orthogonal decomposition and its relationship to dynamic mode decomposition and resolvent analysis , 2017, Journal of Fluid Mechanics.
[31] A. Mohan,et al. A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks , 2018, 1804.09269.
[32] Gary P. Morriss,et al. Statistical Mechanics of Nonequilibrium Liquids , 2008 .
[33] Chidong Zhang,et al. Madden‐Julian Oscillation , 2005 .
[34] Francisco J. Gonzalez,et al. Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems , 2018, ArXiv.
[35] David Duvenaud,et al. Neural Ordinary Differential Equations , 2018, NeurIPS.
[36] E Weinan,et al. Model Reduction with Memory and the Machine Learning of Dynamical Systems , 2018, Communications in Computational Physics.
[37] R. Maulik,et al. Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders , 2020, 2002.00470.