暂无分享,去创建一个
Nan Chen | Honghu Liu | Yingda Li | Honghu Liu | N. Chen | Yingda Li
[1] Geoffrey E. Hinton,et al. Parameter estimation for linear dynamical systems , 1996 .
[2] Andrew J. Majda,et al. An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models , 2014, J. Comput. Phys..
[3] Sarah A. Sheard,et al. Principles of complex systems for systems engineering , 2007, Syst. Eng..
[4] David A. Freedman,et al. Statistical Models: Theory and Practice: References , 2005 .
[5] Karthik Kashinath,et al. Towards physically consistent data-driven weather forecasting: Integrating data assimilation with equivariance-preserving deep spatial transformers , 2021, ArXiv.
[6] Eric Vanden-Eijnden,et al. A computational strategy for multiscale systems with applications to Lorenz 96 model , 2004 .
[7] Traian Iliescu,et al. Data-Driven Filtered Reduced Order Modeling of Fluid Flows , 2017, SIAM J. Sci. Comput..
[8] Changhong Mou,et al. Reduced Order Models for the Quasi-Geostrophic Equations: A Brief Survey , 2020, Fluids.
[9] Reduced-order models for coupled dynamical systems: Data-driven methods and the Koopman operator. , 2020, Chaos.
[10] Peter J. Webster,et al. Climate Science and the Uncertainty Monster , 2011 .
[11] Valerio Lucarini,et al. A proof of concept for scale‐adaptive parametrizations: the case of the Lorenz '96 model , 2016, 1612.07223.
[12] Dmitri Kondrashov,et al. Data-adaptive harmonic spectra and multilayer Stuart-Landau models. , 2017, Chaos.
[13] Mohammad Farazmand,et al. Extreme Events: Mechanisms and Prediction , 2018, Applied Mechanics Reviews.
[14] A. Dembo,et al. Parameter estimation of partially observed continuous time stochastic processes via the EM algorithm , 1992 .
[15] Andrew J. Majda,et al. Lessons in uncertainty quantification for turbulent dynamical systems , 2012 .
[16] Gilead Tadmor,et al. Reduced-Order Modelling for Flow Control , 2013 .
[17] Michael Ghil,et al. Multilevel Regression Modeling of Nonlinear Processes: Derivation and Applications to Climatic Variability , 2005 .
[18] Michael Ghil,et al. Predicting stochastic systems by noise sampling, and application to the El Niño-Southern Oscillation , 2011, Proceedings of the National Academy of Sciences.
[19] Zhong Yi Wan,et al. Reduced-space Gaussian Process Regression for data-driven probabilistic forecast of chaotic dynamical systems , 2016, 1611.01583.
[20] Sebastian Reich,et al. An ensemble Kalman-Bucy filter for continuous data assimilation , 2012 .
[21] Andrew J. Majda,et al. Predicting the cloud patterns of the Madden‐Julian Oscillation through a low‐order nonlinear stochastic model , 2014 .
[22] G. C. Wei,et al. A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms , 1990 .
[23] Andrew J Majda,et al. Concrete ensemble Kalman filters with rigorous catastrophic filter divergence , 2015, Proceedings of the National Academy of Sciences.
[24] Yingda Li,et al. BAMCAFE: A Bayesian machine learning advanced forecast ensemble method for complex turbulent systems with partial observations. , 2021, Chaos.
[25] Simo Särkkä,et al. Bayesian Filtering and Smoothing , 2013, Institute of Mathematical Statistics textbooks.
[26] Alexandre J. Chorin,et al. Discrete approach to stochastic parametrization and dimension reduction in nonlinear dynamics , 2015, Proceedings of the National Academy of Sciences.
[27] Andrew J Majda,et al. An applied mathematics perspective on stochastic modelling for climate , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[28] B. R. Noack. Turbulence, Coherent Structures, Dynamical Systems and Symmetry , 2013 .
[29] Valerio Lucarini,et al. Predicting Climate Change Using Response Theory: Global Averages and Spatial Patterns , 2015, 1512.06542.
[30] J. McWilliams,et al. Stochastic rectification of fast oscillations on slow manifold closures , 2021, Proceedings of the National Academy of Sciences.
[31] Andrew J. Majda,et al. Information theory and stochastics for multiscale nonlinear systems , 2005 .
[33] Timothy DelSole,et al. Predictability and Information Theory. Part II: Imperfect Forecasts , 2005 .
[34] Andrew J. Majda,et al. A mathematical framework for stochastic climate models , 2001 .
[35] Paul N. Edwards,et al. History of climate modeling , 2011 .
[36] D. Wilcox. Multiscale model for turbulent flows , 1986 .
[37] Andrew J. Majda,et al. Non-Gaussian Test Models for Prediction and State Estimation with Model Errors , 2013, Chinese Annals of Mathematics, Series B.
[38] Tim Palmer,et al. A Nonlinear Dynamical Perspective on Climate Prediction , 1999 .
[39] G. Roberts,et al. Data Augmentation for Diffusions , 2013 .
[40] P. Fearnhead,et al. Improved particle filter for nonlinear problems , 1999 .
[41] W. Wong,et al. The calculation of posterior distributions by data augmentation , 1987 .
[42] Duane E. Waliser,et al. Intraseasonal Variability in the Atmosphere-Ocean Climate System , 2005 .
[43] Christopher K. Wikle,et al. Atmospheric Modeling, Data Assimilation, and Predictability , 2005, Technometrics.
[44] E. Kalnay,et al. Ensemble Forecasting at NCEP and the Breeding Method , 1997 .
[45] A. Shiryayev,et al. Statistics of Random Processes Ii: Applications , 2000 .
[46] Gianluigi Rozza,et al. Data-Driven POD-Galerkin Reduced Order Model for Turbulent Flows , 2019, J. Comput. Phys..
[47] D. Nychka. Data Assimilation” , 2006 .
[48] Arno Solin,et al. Expectation maximization based parameter estimation by sigma-point and particle smoothing , 2014, 17th International Conference on Information Fusion (FUSION).
[49] Andrew J. Majda,et al. Physics constrained nonlinear regression models for time series , 2012 .
[50] Zoubin Ghahramani,et al. Learning Nonlinear Dynamical Systems Using an EM Algorithm , 1998, NIPS.
[51] Andrew J. Majda,et al. Information barriers for noisy Lagrangian tracers in filtering random incompressible flows , 2014 .
[52] Omer San,et al. Extreme learning machine for reduced order modeling of turbulent geophysical flows. , 2018, Physical review. E.
[53] Marc Bocquet,et al. Data Assimilation: Methods, Algorithms, and Applications , 2016 .
[54] H. Dijkstra. Nonlinear Climate Dynamics , 2013 .
[55] K. Trenberth,et al. Attribution of climate extreme events , 2015 .
[56] Richard Kleeman,et al. Information Theory and Dynamical System Predictability , 2011, Entropy.
[57] Omer San,et al. Data-driven recovery of hidden physics in reduced order modeling of fluid flows , 2020, Physics of Fluids.
[58] Nan Chen,et al. Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification , 2018, Entropy.
[59] Andrew J. Majda,et al. Test models for improving filtering with model errors through stochastic parameter estimation , 2010, J. Comput. Phys..
[60] Andrew J. Majda,et al. Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation , 2010, J. Comput. Phys..
[61] Andrew J. Majda,et al. Beating the curse of dimension with accurate statistics for the Fokker–Planck equation in complex turbulent systems , 2017, Proceedings of the National Academy of Sciences.
[62] Paul Manneville,et al. Intermittency and the Lorenz model , 1979 .
[63] Dirk P. Kroese,et al. Kernel density estimation via diffusion , 2010, 1011.2602.
[64] Bjørn Eraker. MCMC Analysis of Diffusion Models With Application to Finance , 2001 .
[65] Tim N. Palmer,et al. Ensemble forecasting , 2008, J. Comput. Phys..
[66] I. Moroz,et al. Stochastic parametrizations and model uncertainty in the Lorenz ’96 system , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[67] Nan Chen,et al. Filtering Nonlinear Turbulent Dynamical Systems through Conditional Gaussian Statistics , 2016 .
[68] G. Vallis. Atmospheric and Oceanic Fluid Dynamics: Fundamentals and Large-Scale Circulation , 2017 .
[69] K. Hasselmann. PIPs and POPs: The reduction of complex dynamical systems using principal interaction and oscillation patterns , 1988 .
[70] Andrew J Majda,et al. Mathematical test models for superparametrization in anisotropic turbulence , 2009, Proceedings of the National Academy of Sciences.
[71] I. Mezić,et al. Spectral analysis of nonlinear flows , 2009, Journal of Fluid Mechanics.
[72] M. Ghil,et al. Data assimilation in meteorology and oceanography , 1991 .
[73] G. McLachlan,et al. The EM algorithm and extensions , 1996 .
[74] Dario Ambrosini,et al. Data-driven model predictive control using random forests for building energy optimization and climate control , 2018, Applied Energy.
[75] Valerio Lucarini,et al. Multi-level Dynamical Systems: Connecting the Ruelle Response Theory and the Mori-Zwanzig Approach , 2012, Journal of Statistical Physics.
[76] A. Chattopadhyay,et al. Data‐Driven Super‐Parameterization Using Deep Learning: Experimentation With Multiscale Lorenz 96 Systems and Transfer Learning , 2020, Journal of Advances in Modeling Earth Systems.
[77] Nan Chen,et al. Model Error, Information Barriers, State Estimation and Prediction in Complex Multiscale Systems , 2018, Entropy.
[78] I. J. Myung,et al. Tutorial on maximum likelihood estimation , 2003 .
[79] Adrian Sandu,et al. Efficient Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques , 2015, ArXiv.
[80] Andrew J. Majda,et al. A mechanism for catastrophic filter divergence in data assimilation for sparse observation networks , 2013 .
[81] Sten Bay Jørgensen,et al. Parameter estimation in stochastic grey-box models , 2004, Autom..
[82] Fei Lu,et al. Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism , 2019, J. Comput. Phys..
[83] S. Swain. Handbook of Stochastic Methods for Physics, Chemistry and the Natural Sciences , 1984 .
[84] K. Kashinath,et al. Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence , 2020, CI.
[85] Andrew J. Majda,et al. Efficient nonlinear optimal smoothing and sampling algorithms for complex turbulent nonlinear dynamical systems with partial observations , 2020, J. Comput. Phys..
[86] Mickaël D. Chekroun,et al. Stochastic parameterizing manifolds and non-markovian reduced equations : stochastic manifolds for nonlinear SPDEs II/ Mickaël D. Chekroun, Honghu Liu, Shouhong Wang , 2014 .
[87] Valerio Lucarini,et al. A new framework for climate sensitivity and prediction: a modelling perspective , 2014, Climate Dynamics.
[88] A. Majda. Challenges in Climate Science and Contemporary Applied Mathematics , 2012 .
[89] Andrew J. Majda,et al. Predicting Monsoon Intraseasonal Precipitation using a Low-Order Nonlinear Stochastic Model , 2018 .
[90] Timothy DelSole,et al. Predictability and Information Theory. Part I: Measures of Predictability , 2004 .
[91] Benjamin Peherstorfer,et al. Dynamic data-driven reduced-order models , 2015 .
[92] Andrew J. Majda,et al. Stochastic superparameterization in quasigeostrophic turbulence , 2013, J. Comput. Phys..
[93] O. San,et al. Data-Driven Variational Multiscale Reduced Order Models , 2020, ArXiv.
[94] Nan Chen,et al. Rigorous Analysis for Efficient Statistically Accurate Algorithms for Solving Fokker-Planck Equations in Large Dimensions , 2017, SIAM/ASA J. Uncertain. Quantification.
[95] G. Evensen. Data Assimilation: The Ensemble Kalman Filter , 2006 .
[96] K. Vahala. Handbook of stochastic methods for physics, chemistry and the natural sciences , 1986, IEEE Journal of Quantum Electronics.
[97] Andrew J. Majda,et al. Stochastic superparameterization in a one-dimensional model for wave turbulence , 2014 .
[98] Andrew J. Majda,et al. New perspectives on superparameterization for geophysical turbulence , 2014, J. Comput. Phys..
[99] A. Majda. Introduction to PDEs and Waves in Atmosphere and Ocean , 2003 .
[100] D. Randall,et al. A Multiscale Modeling System: Developments, Applications, and Critical Issues , 2009 .
[101] Matthew C. Coleman,et al. Bayesian parameter estimation with informative priors for nonlinear systems , 2006 .
[102] R. Salmon,et al. Lectures on Geophysical Fluid Dynamics , 1998 .
[103] Andrew J. Majda,et al. Filtering Complex Turbulent Systems , 2012 .
[104] Luc Van Gool,et al. An adaptive color-based particle filter , 2003, Image Vis. Comput..
[105] Tim N. Palmer,et al. A nonlinear dynamical perspective on climate change , 1993 .
[106] Darren J. Wilkinson,et al. Bayesian inference for nonlinear multivariate diffusion models observed with error , 2008, Comput. Stat. Data Anal..
[107] Michael Ghil,et al. Data-driven non-Markovian closure models , 2014, 1411.4700.
[108] Nan Chen,et al. Efficient statistically accurate algorithms for the Fokker-Planck equation in large dimensions , 2017, J. Comput. Phys..
[109] Frank Kwasniok,et al. The reduction of complex dynamical systems using principal interaction patterns , 1996 .
[110] S. Childress,et al. Topics in geophysical fluid dynamics. Atmospheric dynamics, dynamo theory, and climate dynamics. , 1987 .
[111] R. E. Kalman,et al. New Results in Linear Filtering and Prediction Theory , 1961 .
[112] Andrew J. Majda,et al. Low-Frequency Climate Response and Fluctuation–Dissipation Theorems: Theory and Practice , 2010 .
[113] Charbel Farhat,et al. The GNAT method for nonlinear model reduction: Effective implementation and application to computational fluid dynamics and turbulent flows , 2012, J. Comput. Phys..
[114] S. Brunton,et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.
[115] P. Schmid,et al. Dynamic mode decomposition of numerical and experimental data , 2008, Journal of Fluid Mechanics.
[116] Nan Chen,et al. Learning nonlinear turbulent dynamics from partial observations via analytically solvable conditional statistics , 2020, J. Comput. Phys..
[117] Eric Vanden-Eijnden,et al. Subgrid-Scale Parameterization with Conditional Markov Chains , 2008 .
[118] Vanden Eijnden E,et al. Models for stochastic climate prediction. , 1999, Proceedings of the National Academy of Sciences of the United States of America.
[119] Andrew J. Majda,et al. Introduction to Turbulent Dynamical Systems in Complex Systems , 2016 .
[120] Fredrik Gustafsson,et al. On Resampling Algorithms for Particle Filters , 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.
[122] P. N. Edwards. GLOBAL CLIMATE SCIENCE, UNCERTAINTY AND POLITICS: DATA-LADEN MODELS, MODEL-FILTERED DATA , 1999 .
[123] Xin Yan,et al. Linear Regression Analysis: Theory and Computing , 2009 .
[124] Karthik Duraisamy,et al. Modal Analysis of Fluid Flows: Applications and Outlook , 2019, AIAA Journal.
[125] Extreme events in turbulent flow , 2021, Journal of Fluid Mechanics.