Publications in chronological order

Books

  1. Nan Chen, Stochastic Methods for Modeling and Predicting Complex Dynamical Systems --- Uncertainty Quantification, State Estimation and Reduced-Order Models, Synthesis Lectures on Mathematics & Statistics, Springer, 2023.

Journal Articles (* indicates the corresponding author, underline indicates the students and postdocs I supervised. Usually a student/postdoc of mine will be given the first or corresponding author. The first author and the corresponding author are usually the main contributors. )

2024 (including under revision and submitted)

  1. Yinling Zhang, Nan Chen*, Jerome Vialard, and Xianghui Fang, A Physics-Informed Auto-Learning Framework for Developing Stochastic Conceptual Models for ENSO Diversity, Submitted, 2024
  2. Quanling Deng, Nan Chen*, Samuel Stechmann, and Jiuhua Hu, LEMDA: A Lagrangian-Eulerian Multiscale Data Assimilation Framework, Submitted, 2024.
  3. Erik Bollt, Nan Chen*, Stephen Wiggins, A Causation-Based Computationally Efficient Strategy for Deploying Lagrangian Drifters to Improve Real-Time State Estimation, Submitted, 2024.
  4. Marios Andreou and Nan Chen*. Statistical Response of ENSO Complexity to Initial Value and Model Parameter Perturbations, Submitted, 2024.
  5. Jinyu Wang, Xianghui Fang*, Nan Chen, Mu Mu, Insights of Dynamic Forcing Effects of MJO on ENSO from a Shallow Water Model, Submitted, 2024.
  6. Nan Chen*, Evelyn Lunasin, and Stephen Wiggins. Lagrangian Descriptors with Uncertainty. Physica D, Under Revision, 2023.
  7. Nan Chen and Di Qi*, A Physics-Informed Data-Driven Algorithm for Ensemble Forecast of Complex Turbulent Systems, Applied Mathematics and Computation, 466 (2024): 128480.
  8. Nan Chen*, Evelyn Lunasin, and Stephen Wiggins. Launching Drifter Observations in the Presence of Uncertainty, Physica D, (2024): 134086.

2023

  1. Jeffrey Covington, Di Qi*, and Nan Chen. Effective Statistical Control Strategies for Complex Turbulent Dynamical Systems. Proceedings of the Royal Society A, Acepted, 2023.
  2. Nan Chen and Di Qi*, A Physics-Informed Data-Driven Algorithm for Ensemble Forecast of Complex Turbulent Systems, Under Minor Revision, 2023.
  3. Changhong Mou, Nan Chen*, and Traian Iliescu, An Efficient Data-Driven Multiscale Stochastic Reduced Order Modeling Framework for Complex Turbulent Systems Journal of Computational Physics, 493 (2023): 112450.
  4. Changhong Mou, Leslie Smith, Nan Chen*, Combining Stochastic Parameterized Reduced-Order Models with Machine Learning for Data Assimilation and Uncertainty Quantification with Partial Observations, Journal of Advances in Modeling Earth Systems, 15.10 (2023): e2022MS003597.
  5. Quanling Deng, Samuel N. Stechmann, and Nan Chen, Particle-Continuum Multiscale Modeling of Sea Ice Floes, SIAM MMS, Accepted, 2023.
  6. Xianghui Fang and Nan Chen*, Quantifying the Predictability of ENSO Complexity Using a Statistically Accurate Multiscale Stochastic Model and Information Theory, Journal of Climate, (2023): 2681-2702.
  7. Nan Chen and Yinling Zhang*, A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization, Physica D: Nonlinear Phenomena, 449 (2023): 133743.
  8. Nan Chen and Xianghui Fang*, A Simple Multiscale Intermediate Coupled Stochastic Model for El Ni\~no Diversity and Complexity, Journal of Advances in Modeling Earth Systems, 15.4 (2023): e2022MS003469.
  9. Nan Chen and Shubin Fu*, Nonlinear Lagrangian Data Assimilation with Linear Stochastic Forecast Model, Physica D: Nonlinear Phenomena 452 (2023): 133784.
  10. Nan Chen and Yinling Zhang*, Rigorous Derivation of Stochastic Conceptual Models for the El Ni\~no-Southern Oscillation from a Spatially-Extended Dynamical System, Physica D: Nonlinear Phenomena (2023): 133842.
  11. Yinling Zhang, Nan Chen*, Curt A. Bronkhorst, Hansohl Cho, and Robert Argus, Data-Driven Statistical Reduced-Order Modeling and Quantification of Polycrystal Mechanics Leading to Porosity-Based Ductile Damage, Journal of the Mechanics and Physics of Solids, 179, 105386, 2023.
  12. Chuanqi Chen, Nan Chen, and Jinlong Wu*, CEBoosting: Online Sparse Identification of Dynamical Systems with Regime Switching by Causation Entropy Boosting, Chaos 33, 083114 (2023).

2022

  1. Nan Chen, Xianghui Fang* and Jin-Yi Yu, A Multiscale Model for El Nino Complexity, Nature Partner Journals Climate and Atmospheric Science, 5.1 (2022): 1-13.
  2. Tabea Gleiter, Tijana Janjic* and Nan Chen, Ensemble Kalman Filter based Data Assimilation for Tropical Waves in the MJO Skeleton Model, Quarterly Journal of the Royal Meteorological Society, 148.743 (2022): 1035-1056.
  3. Nan Chen, Shubin Fu* and Georgy Manucharyan, An Efficient and Statistically Accurate Lagrangian DataAssimilation Algorithm with Applications to Discrete Element Sea Ice Models, Journal of Computational Physics, 455 (2022): 111000.
  4. Nan Chen, Quanling Deng * and Samuel Stechmann, Lagrangian Data Assimilation and Uncertainty Quantification for Sea Ice Floes with Efficient Superfloe Parameterization, Accepted and published online, SIAM/ASA Journal of Uncertainty Quantification, 2022.
  5. Ludovico T Giorgini, Woosok Moon, Nan Chen, John S Wettlaufer*, A Non-Gaussian Stochastic Model for the El Nino Southern Oscillation, Physical Review Research, 4, L022065, 2022.
  6. Nan Chen, Honghu Liu, and Fei Lu*, Shock trace prediction by reduced models for a viscous stochastic Burgers equation, Chaos: An Interdisciplinary Journal of Nonlinear Science, 32.4 (2022): 043109.
  7. Nan Chen, Yingda Li *, and Honghu Liu, Conditional Gaussian Nonlinear System: a Fast Preconditioner and a Cheap Surrogate Model For Complex Nonlinear Systems, Chaos: An Interdisciplinary Journal of Nonlinear Science, 32.5 (2022): 053122. [Editor's Pick].
  8. Jeffrey Covington, Nan Chen*, and Monica M. Wilhelmus, Bridging Gaps in the Climate Observation Network: A Physics-based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Models, Journal of Advances in Modeling Earth Systems, Accepted, 2022MS003218, 2022.
  9. Nan Chen and Aseel Farhat* and Evelyn Lunasin, Data Assimilation With Model Error: Analytical and Computational Study for Sabra Shell Model, Physica D: Nonlinear Phenomena, (2022): 133552.

2021

  1. Qiu Yang*, Andrew J Majda, and Nan Chen, ENSO Diversity in a Tropical Stochastic Skeleton Model for the MJO, El Nino, and Dynamic Walker Circulation, Journal of Climate, 1-56, 2021.
  2. Xiao Hou, Song Gao*, Qin Li, Yuhao Kang, Nan Chen, Kaiping Chen, Jinmeng Rao, Jordan S. Ellenberg, and Jonathan A. Patz, Intra-county modeling of COVID-19 infection with human mobility: assessing spatial heterogeneity with business traffic, age and race, Proc. Natl. Acad. Sci, 118.24 2021.
  3. Nan Chen*, Faheem Gilani and John Harlim, A Bayesian Machine Learning Algorithm for Predicting ENSO Using Short Observational Time Series, Geophysical Research Letters, 48(17), e2021GL093704, 2021.
  4. Ziheng Zhang, and Nan Chen*. "Parameter Estimation of Partially Observed Turbulent Systems Using Conditional Gaussian Path-Wise Sampler." Computation 9.8 2021.
  5. Nan Chen, Shubin Fu* and Georgy Manucharyan, Lagrangian Data Assimilation and Parameter Estimation of a Simple Sea Ice Floe Model, Journal of Advances in Modeling Earth Systems, 13.10 (2021): e2021MS002513.
  6. Nan Chen, Yuchen Li and Evelyn Lunasin*, An Efficient Continuous Time Data Assimilation Algorithm for the Sabra Shell Models, Chaos: An Interdisciplinary Journal of Nonlinear Science, 31, 103123, 2021.
  7. Nan Chen and Yingda Li*, BAMCAFE: A BAyesian MaChine learning Advanced Forecast Ensemble method for complex nonlinear systems with partial observations, Chaos: An Interdisciplinary Journal of Nonlinear Science, 31, 113114, 2021.

2020

  1. Nan Chen*, Can Short and Partial Observations Reduce Model Error and Facilitate Machine Learning Prediction?, Entropy, 22.10 (2020): 1075.
  2. Nan Chen*, Learning Nonlinear Turbulent Dynamics from Partial Observations via Analytically Solvable Conditional Statistics, Journal of Computational Physics, 109635, 2020.
  3. Nan Chen*, Improving the Prediction Skill of Complex Nonlinear Dynamical Systems Using Nonlinear Smoothing and Filtering Techniques, Research in the Mathematical Sciences, 7(18), 2020.
  4. Nan Chen*, An Information Criterion for Choosing Observation Locations in Data Assimilation and Prediction, SIAM/ASA Uncertainty Quantification, 8.4 (2020): 1548- 1573.
  5. Nan Chen* and Andrew J Majda, Efficient Nonlinear Optimal Smoothing and Sampling Algorithms for Complex Turbulent Nonlinear Dynamical Systems with Partial Observations, Journal of Computational Physics, 109381., 2020.
  6. Nan Chen* and Andrew J Majda, Predicting Observed and Hidden Extreme Events in Complex Nonlinear Dynamical Systems with Partial Observations and Short Training Time Series, Chaos: An Interdisciplinary Journal of Nonlinear Science, 30.3: 033101., 2020.

2019

  1. Reed. H. Ogrosky*, Samuel. N. Stechmann, Nan Chen and Andrew J Majda, Singular Spectrum Analysis with Conditional Predictions for Real-Time State Estimation and Forecasting, Geophysical Research Letters, 46.3 (2019): 1851-1860.
  2. Nan Chen*, Xiao Hou, Qin Li and Yingda Li, Understanding and Predicting Nonlinear Turbulent Dynamical Systems with Information Theory, Atmosphere, 10.5 (2019): 248.
  3. Nan Chen*, Andrew J Majda and Xin Tong, Spatial Localization for Nonlinear Dynamical Stochastic Models for Excitable Media, Chinese Annals of Mathematics, Series B (Special volume on the occasion of Professor Andrew Majda's 70th birthday), Accepted, 2019
  4. C. T. Sabeerali, Ajayamohan Ravindran*, Hamza Kunhu Bangalath and Nan Chen, Atlantic Zonal Mode: An Emerging Source of Indian Summer Monsoon Variability in a Warming World, Geophysical Research Letters, 46.8 (2019): 4460-4467.
  5. Nan Chen* and Andrew J Majda, A New Efficient Parameter Estimation Algorithm for High-Dimensional Complex Nonlinear Turbulent Dynamical Systems with Partial Observations, Accepted, 2019.
  6. Sulian Thual, Andrew J. Majda, Nan Chen*, Statistical occurrence and mechanisms of the 2014–2016 delayed super El Nino captured by a simple dynamical model., Climate Dynamics, 52 (2019): 2351-2366.

2018

  1. Nan Chen* and Andrew J. Majda, Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification, Entropy, 2018, 20(7), 509.[PDF]
  2. Andrew J. Majda and Nan Chen*, Model Error, Information Barriers, State Estimation and Prediction in Complex Multiscale Systems, 2018, Entropy, 2018, 20(9), 644.. [PDF]
  3. Sulian Thual, Andrew J. Majda and Nan Chen*, A Tropical Stochastic Skeleton Model for the MJO, El Nino and Dynamic Walker Circulation: A Simplied GCM, 2018, Journal of Climate, 31.22 (2018): 9261-9282. [PDF]
  4. Nan Chen*, Andrew J. Majda and Xin Tong, Rigorous Analysis for Efficient Statistically Accurate Algorithms for the Fokker-Planck Equation in Large Dimensions, SIAM/ASA Journal on Uncertainty Quantification, 2018, 6.3 (2018): 1198-1223. [PDF]
  5. Nan Chen*, Andrew J. Majda, C. T. Sabeerali and Ajayamohan S. Ravindran, Predicting Monsoon Intraseasonal Precipitation using a Low-Order Nonlinear Stochastic Model, Journal of Climate, 2018, 31.11 (2018): 4403-4427.[Link]
  6. Nan Chen*, Andrew J. Majda and Sulian Thual, Observations and Mechanisms of a Simple Stochastic Dynamical Model Capturing El Nino Diversity, Journal of Climate, 2018, 31.1 (2018): 449-471. [PDF]
  7. Nan Chen* and Andrew J. Majda, Efficient Statistically Accurate Algorithms for the Fokker-Planck Equation in Large Dimensions, Journal of Computational Physics, 354 2018: 242-268. [PDF]

2017

  1. 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]
  2. Sulian Thual*, Andrew J. Majda, Nan Chen, Seasonal Synchronization of a Simple Stochastic Dynamical Model Capturing El Nino Diversity, Journal of Climate, 30.24 (2017): 10047-10066.[Link]
  3. 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]

2016

  1. 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]
  2. 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]
  3. 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]
  4. 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]
  5. 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]
  6. 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]

2015

  1. 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 Forecasting, 1(1), pp. 1-20, 2015. [PDF]
  2. 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]
  3. 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]

2014

  1. 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]
  2. 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]
  3. 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]

2013

  1. 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]
  2. Wei Yao*, Yabei Li, and Nan Chen, Analytic solutions of the interstitial fluid flow models, Journal of Hydrodynamics, Ser. B, Vol. 25(5), pp. 683-694, 2013.

2012

  1. 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]
  2. 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]
  3. Nan Chen*, Min Zhong, Boxi Xu, Clustering Method with Regularization for Time Series Data and Its Application, Journal of Fudan University (Natural Science), Vol. 51(4) pp. 450-457, 2012 (in Chinese).

Before 2011

  1. Zuicha Deng*, Liu Yang and Nan Chen, Uniqueness and stability of the minimizer for a binary functional arising in an inverse heat conduction problem, Journal of Mathematical Analysis and Applications, 382(1), pp 474-486, 2011.
  2. 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]
  3. Wei Yao*, Nan Chen, and Guanghong Ding, Numerical simulation of interstitial fluid based on a new view of starling's hypothesis of capillary wall, Chinese Journal of Theoretic and Applied Mechanics, Vol. 41(1), pp. 35-40, 2009 (in Chinese).

Translated Books

  1. 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

  1. Nan Chen*, Sulian Thual and Malte F. Stuecker, El Nino and the Southern Oscillation: Theory, Elsevier Earth Systems and Environmental Sciences, 2019.
  2. Nan Chen*, Sulian Thual and Shineng Hu, El Nino and the Southern Oscillation: Observations, Elsevier Earth Systems and Environmental Sciences, 2019.
  3. 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]

Conference Papers

  1. Ludovico Giorgini*, Soon Hoe Lim, Woosok Moon, Nan Chen and John Wettlaufer, Modeling the El Niño Southern Oscillation with Neural Differential Equations, ICML 2021 Time Series Workshop.

 

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