Selected Publications

(See my CV for a full list of publications and see "Research" page for detailed descriptions)

(See in here for a list of publications in chronological order)

Articles (* indicates the corresponding author)

I. Efficient statistically accurate algorithms for solving high dimensional Fokker-Planck equation

**Nan Chen*** and Andrew J. Majda, Beating the Curse of Dimension with Accurate Statistics for the Fokker-Planck Equation in Complex Turbulent Systems,*Proc. Natl. Acad. Sci,*2017: 201717017. [PDF]**Nan Chen***, Andrew J. Majda and Xin Tong, Rigorous Analysis for Efficient Statistically Accurate Algorithms for the Fokker-Planck Equation in Large Dimensions, Submitted to*SIAM/ASA Journal on Uncertainty Quantification*. [PDF][Supp]**Nan Chen*** and Andrew J. Majda, Efficient Statistically Accurate Algorithms for the Fokker-Planck Equation in Large Dimensions,*Journal of Computational Physics*,

II. Simple stochastic dynamical models for the El Nino Southern Oscillation (ENSO)

- Sulian Thual, Andrew J. Majda,
**Nan Chen***, Mechanisms of the 2014-2016 Delayed Super El Nino Capturing by Simple Dynamical Models, Submitted to*Climate Dynamics*, 2017. - Sulian Thual*, Andrew J. Majda,
**Nan Chen**, Seasonal Synchronization of a Simple Stochastic Dynamical Model Capturing El Nino Diversity,*Journal of Climate*, 2017, Accepted. **Nan Chen***, Andrew J. Majda and Sulian Thual, Observations and Mechanisms of a Simple Stochastic Dynamical Model Capturing El Nino Diversity,*Journal of Climate*, 2017, Accepted. [PDF]**Nan Chen*** and Andrew J. Majda, A Simple Stochastic Dynamical Model Capturing the Statistical Diversity of El Nino Southern Oscillation,*Proc. Natl. Acad. Sci.*, 114(7), pp. 1468-1473, 2017. [PDF]**Nan Chen*** and Andrew J. Majda, A Simple Dynamical Model Capturing the Key Features of the Central Pacific El Nino,*Proc. Natl. Acad. Sci.*, 113(42), pp. 11732-11737, 2016. [PDF] [SI Appendix]- Sulian Thual, Andrew J. Majda,
**Nan Chen*** and Sam Stechmann, A Simple Stochastic Model for El Nino with Westerly Wind Bursts,*Proc. Natl. Acad. Sci.*, 113(37), pp. 10245-10250, 2016. [PDF] [SI Appendix]

III. Predicting the MJO and monsoon intraseasonal time series through nonlinear low-order stochastic models

**Nan Chen***, Andrew J. Majda, C. T. Sabeerali and Ajayamohan S. Ravindran, Predicting Monsoon Intraseasonal Precipitation using a Low-Order Nonlinear Stochastic Model, Submitted to*Journal of Climate*.**Nan Chen*** and Andrew J. Majda, Predicting the Cloud Patterns for the Boreal Summer Intraseasonal Oscillation through a Low-Order Nonlinear Stochastic Model*, Mathematics of Climate and Weather Forecastin*g, 1(1), pp. 1-20, 2015. [PDF]**Nan Chen*** and Andrew J. Majda, Predicting the Real-time Multivariate Index for the Madden-Julian Oscillation through a Low-Order Nonlinear Stochastic Model*, Monthly Weather Review*, 143(6), pp. 2148-2169, 2015. [PDF]**Nan Chen***, Andrew J. Majda, and Dimitris Giannakis, Predicting the Cloud Patterns for the Madden-Julian Oscillation through a Low-Order Nonlinear Stochastic Model,*Geophysical Research Letters*, 41(15), pp. 5612-5619, 2014. [PDF] [Supp] [Movie]

IV. Filtering, state estimation and predicting conditional Gaussian systems with model error (dyad model, triad model, parameter estimation and noisy Lagrangian tracers)

**Nan Chen*** and Andrew J. Majda, Filtering Nonlinear Turbulent Dynamical Systems through Conditional Gaussian Statistics,*Monthly Weather Review*, 144(12), pp. 4885-4917, 2016. [PDF]**Nan Chen*** and Andrew J. Majda, Model Error in Filtering Random Compressible Flows Using Noisy Lagrangian Tracers,,*Monthly Weather Review*, 144(11), pp. 4037-4061, 2016. [PDF]**Nan Chen**, Andrew J. Majda and Xin Tong*, Noisy Lagrangian Tracers for Filtering Random Rotating Compressible Flows*,**Journal of Nonlinear Science*, 25(3), pp. 451-488, 2015. [PDF]**Nan Chen**, Andrew J. Majda and Xin Tong*, Information Barriers for Noisy Lagrangian Tracers in Filtering Random Incompressible Flows,*Nonlinearity*, 27(9), pp. 2133-2163, 2014. [PDF]

V. Filtering the spatial-extended stochastic systems with model error

**Nan Chen*** and Andrew J. Majda, Filtering the Stochastic Skeleton Model for the Madden-Julian Oscillation*,**Monthly Weather Review*, 144(2), pp. 501-527, 2016. [PDF]

VI. MCMC algorithm for parameter estimation with hidden instability

**Nan Chen***, Dimitris Giannakis, Radu Herbei and Andrew J. Majda, An MCMC Algorithm for Parameter Estimation of Signals with Hidden Intermittent Instability,*SIAM/ASA Journal of Uncertainty Quantification*, 2(1), pp. 647-669, 2014. [PDF]

VII. Test models for prediction and state estimation with model errors

- Michal Branicki*,
**Nan Chen**, and Andrew Majda, Non-Gaussian Test Models for Prediction and State Estimation with Model Errors*,**Chinese Annals of Mathematics, Series B*, Volume 34 (Special volumn in honor of the scientific heritage of Jacques-Louis Lions), Issue 1, pp. 29-64, 2013. [PDF]

VIII. Ground water system

**Nan Chen**, Max Gunzburger, Bill Hu, Xiaoming Wang*, and Celestine Woodruff, Calibrating the exchange coefficient in the modified coupled continuum pipe-flow model for flows in karst aquifers*, Journal of Hydrology*, 414-415, pp. 294-301, 2012. [PDF]- Shuai Lu*,
**Nan Chen**, Bang Hu and Jin Cheng, On the Inverse Problems for the Coupled Continuum Pipe Flow model for flows in karst aquifer,*Inverse Problems*, 28, pp. 065003, 2012. [Link] **Nan Chen***, Max Gunzburger, and Xiaoming Wang*,*Asymptotic Analysis of the Differences between the Stokes-Darcy System with Different Interface Conditions and the Stokes-Brinkman System*,**J. Math. Anal. Appl.*, 368,(2), pp. 658-676, 2010.[PDF]

IX. Gevrey regularity

**Nan Chen**, Cheng Wang*, and Steven Wise, Global in time Gevrey regularity solution for a class of bistable gradient flows*,**Discrete and Continuous Dynamical Systems-Series B*, 21(6), 1689-1711, 2016. [PDF]

Translated Books

**Nan Chen**, Xiaoming Wang, Jin Cheng, and Yu Jiang*,*Introduction to PDEs and Waves for the Atmosphere and Ocean (written by Prof. A. Majda), Translated Chinese version,*Science Press*, Oct 2009. [Link]

Book chapters

- Michal Branicki,
**Nan Chen**and Andrew Majda, Page 99-138. P. G. Ciarlet, T. Li and Y. Maday,*Partial Differential Equations: Theory, Control and Approximation*. Springer, 2014. [Link]