Data-driven stochastic model for cross-interacting processes with different time scales

An Atmospheric Signal Lowering the Spring Predictability Barrier in Statistical ENSO Forecasts

(2021) Dmitry Mukhin et al.   GEOPHYSICAL RESEARCH LETTERS

Combining machine learning and data assimilation to forecast dynamical systems from noisy partial observations

(2021) Georg A. Gottwald et al.   CHAOS

Data-adaptive harmonic analysis of oceanic waves and turbulent flows

(2020) D. Kondrashov et al.   CHAOS

Machine Learning for Prediction with Missing Dynamics

(2020) John Harlim et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Simulation of Complex Systems Using the Observed Data Based on Recurrent Artificial Neural Networks

(2019) A. F. Seleznev et al.   Radiophysics and Quantum Electronics

Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition

(2019) Dmitry Mukhin et al.   Scientific Reports

Bayesian framework for simulation of dynamical systems from multidimensional data using recurrent neural network

(2019) Aleksei Seleznev et al.   CHAOS

Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control

(2018) Milan Korda et al.   AUTOMATICA

Linear dynamical modes as new variables for data-driven ENSO forecast

(2018) Andrey Gavrilov et al.   CLIMATE DYNAMICS

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks

(2018) Pantelis R. Vlachas et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Deep echo state networks with uncertainty quantification for spatio-temporal forecasting

(2018) Patrick L. McDermott et al.   ENVIRONMETRICS

Simulation of the modern climate using the INM-CM48 climate model

(2018) Evgenii M. Volodin et al.   RUSSIAN JOURNAL OF NUMERICAL ANALYSIS AND MATHEMATICAL MODELLING

Simulation of modern climate with the new version of the INM RAS climate model

(2017) E. M. Volodin et al.   IZVESTIYA ATMOSPHERIC AND OCEANIC PHYSICS

Marginally Stable Triangular Recurrent Neural Network Architecture for Time Series Prediction

(2017) Seshadri Sivakumar et al.   IEEE Transactions on Cybernetics

Method for reconstructing nonlinear modes with adaptive structure from multidimensional data

(2016) Andrey Gavrilov et al.   CHAOS

A Bayesian Hidden Markov Model of Daily Precipitation over South and East Asia

(2016) Tracy Holsclaw et al.   JOURNAL OF HYDROMETEOROLOGY

Stochastic modeling of decadal variability in ocean gyres

(2015) D. Kondrashov et al.   GEOPHYSICAL RESEARCH LETTERS

Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models

(2015) Dmitry Mukhin et al.   JOURNAL OF CLIMATE

Predicting Critical Transitions in ENSO Models. Part I: Methodology and Simple Models with Memory

(2015) Dmitry Mukhin et al.   JOURNAL OF CLIMATE

A Data–Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition

(2015) Matthew O. Williams et al.   JOURNAL OF NONLINEAR SCIENCE

Principal nonlinear dynamical modes of climate variability

(2015) Dmitry Mukhin et al.   Scientific Reports

Random dynamical models from time series

(2012) Y. I. Molkov et al.   PHYSICAL REVIEW E

Prognosis of qualitative system behavior by noisy, nonstationary, chaotic time series

(2011) Y. I. Molkov et al.   PHYSICAL REVIEW E

Predicting stochastic systems by noise sampling, and application to the El Nino-Southern Oscillation

(2011) M. D. Chekroun et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Dynamic mode decomposition of numerical and experimental data

(2010) PETER J. SCHMID   JOURNAL OF FLUID MECHANICS

Reduced models of atmospheric low-frequency variability: Parameter estimation and comparative performance

(2009) K. Strounine et al.   PHYSICA D-NONLINEAR PHENOMENA

Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series

(2009) Ya. I. Molkov et al.   PHYSICAL REVIEW E

Markov chain Monte Carlo method in Bayesian reconstruction of dynamical systems from noisy chaotic time series

(2008) E. M. Loskutov et al.   PHYSICAL REVIEW E