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Dmitry Mukhin
Dmitry Mukhin
Institute of Applied Physics of RAS, Nizhny Novgorod, Russia
Verified email at ipfran.ru
Title
Cited by
Cited by
Year
Principal nonlinear dynamical modes of climate variability.
D Mukhin, A Gavrilov, A Feigin, E Loskutov, J Kurths
Scientific reports 5, 15510-15510, 2015
54*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
512009
Random dynamical models from time series
YI Molkov, EM Loskutov, DN Mukhin, AM Feigin
Physical Review E 85 (3), 036216, 2012
452012
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
402015
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
382006
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
352019
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
322016
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
32*2011
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
282015
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
262008
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
222001
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
212019
Bayesian optimization of empirical model with state-dependent stochastic forcing
A Gavrilov, E Loskutov, D Mukhin
Chaos, Solitons & Fractals 104, 327-337, 2017
202017
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
182019
Bayesian approach to retrieval of vertical ozone profile from radiometry data
DN Mukhin, AM Feigin, YI Molkov, EV Suvorov
Advances in Space Research 37 (12), 2292-2298, 2006
182006
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
152002
An atmospheric signal lowering the spring predictability barrier in statistical ENSO forecasts
D Mukhin, A Gavrilov, A Seleznev, M Buyanova
Geophysical Research Letters 48 (6), e2020GL091287, 2021
142021
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
142018
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
122016
Improving statistical prediction and revealing nonlinearity of ENSO using observations of ocean heat content in the tropical Pacific
A Seleznev, D Mukhin
Climate Dynamics 60 (1), 1-15, 2023
92023
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Articles 1–20