| Principal nonlinear dynamical modes of climate variability D Mukhin, A Gavrilov, A Feigin, E Loskutov, J Kurths Scientific reports 5 (1), 15510, 2015 | 52 | 2015 |
| Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series YI Molkov, DN Mukhin, EM Loskutov, AM Feigin, GA Fidelin Physical Review E 80 (4), 046207, 2009 | 52 | 2009 |
| Random dynamical models from time series YI Molkov, EM Loskutov, DN Mukhin, AM Feigin Physical Review E 85 (3), 036216, 2012 | 45 | 2012 |
| Predicting critical transitions in ENSO models. Part II: Spatially dependent models D Mukhin, D Kondrashov, E Loskutov, A Gavrilov, A Feigin, M Ghil Journal of Climate 28 (5), 1962-1976, 2015 | 40 | 2015 |
| Modified Bayesian approach for the reconstruction of dynamical systems from time series DN Mukhin, AM Feigin, EM Loskutov, YI Molkov Physical Review E 73 (3), 036211, 2006 | 38 | 2006 |
| Linear dynamical modes as new variables for data-driven ENSO forecast A Gavrilov, A Seleznev, D Mukhin, E Loskutov, A Feigin, J Kurths Climate Dynamics 52, 2199-2216, 2019 | 36 | 2019 |
| Method for reconstructing nonlinear modes with adaptive structure from multidimensional data A Gavrilov, D Mukhin, E Loskutov, E Volodin, A Feigin, J Kurths Chaos: An Interdisciplinary Journal of Nonlinear Science 26 (12), 2016 | 32 | 2016 |
| Predicting critical transitions in ENSO models. Part I: Methodology and simple models with memory D Mukhin, E Loskutov, A Mukhina, A Feigin, I Zaliapin, M Ghil Journal of Climate 28 (5), 1940-1961, 2015 | 28 | 2015 |
| Prognosis of qualitative system behavior by noisy, nonstationary, chaotic time series YI Molkov, DN Mukhin, EM Loskutov, RI Timushev, AM Feigin Physical review E 84 (3), 036215, 2011 | 27 | 2011 |
| Markov chain Monte Carlo method in Bayesian reconstruction of dynamical systems from noisy chaotic time series EM Loskutov, YI Molkov, DN Mukhin, AM Feigin Physical Review E 77 (6), 066214, 2008 | 26 | 2008 |
| Bayesian framework for simulation of dynamical systems from multidimensional data using recurrent neural network A Seleznev, D Mukhin, A Gavrilov, E Loskutov, A Feigin Chaos: An Interdisciplinary Journal of Nonlinear Science 29 (12), 2019 | 22 | 2019 |
| Prognosis of qualitative behavior of a dynamic system by the observed chaotic time series AM Feigin, YI Molkov, DN Mukhin, EM Loskutov Radiophysics and quantum electronics 44, 348-367, 2001 | 22 | 2001 |
| Bayesian optimization of empirical model with state-dependent stochastic forcing A Gavrilov, E Loskutov, D Mukhin Chaos, Solitons & Fractals 104, 327-337, 2017 | 20 | 2017 |
| Bayesian data analysis for revealing causes of the middle Pleistocene transition D Mukhin, A Gavrilov, E Loskutov, J Kurths, A Feigin Scientific Reports 9 (1), 7328, 2019 | 18 | 2019 |
| Investigation of nonlinear dynamical properties by the observed complex behaviour as a basis for construction of dynamical models of atmospheric photochemical systems AM Feigin, YI Molkov, DN Mukhin, EM Loskutov Faraday Discussions 120, 105-123, 2002 | 15 | 2002 |
| Nonlinear reconstruction of global climate leading modes on decadal scales D Mukhin, A Gavrilov, E Loskutov, A Feigin, J Kurths Climate dynamics 51, 2301-2310, 2018 | 14 | 2018 |
| Empirical mode with a variable spatial-temporal structure and the dynamics of superradiant lasers ER Kocharovskaya, AS Gavrilov, VV Kocharovsky, EM Loskutov, ... Journal of Physics: Conference Series 740 (1), 012007, 2016 | 12 | 2016 |
| Principal nonlinear dynamical modes of climate variability. Sci. Rep., 5, 15510 D Mukhin, A Gavrilov, A Feigin, E Loskutov, J Kurths | 7 | 2015 |
| Prognosis of qualitative behavior of a system by noisy chaotic time-series AM Feigin, DN Mukhin, I Molkov Ya, EM Loskutov, RI Timushev Phys. Rev. E, 2011 | 5 | 2011 |
| Spectral-Dynamical Peculiarities of Polarization of the Active Medium and Space-Time Empirical Modes of a Laser with a Low-Q Cavity EP Kocharovskaya, AS Gavrilov, VV Kocharovsky, EM Loskutov, ... Radiophysics and Quantum Electronics 61, 806-833, 2019 | 4 | 2019 |
