Research Interests

From Chekroun et al. (2011), Physica D, 240 (21): 1685-1700:

 

Vimeo movie: https://vimeo.com/240039610

 

Recent Publications

Kondrashov, Dmitri, and Mickaël D. Chekroun. 2018. “Data-adaptive harmonic analysis and modeling of solar wind-magnetosphere coupling.” Journal of Atmospheric and Solar-Terrestrial Physics 177: 179-189. Publisher's Version Abstract

The solar wind-magnetosphere coupling is studied by new data-adaptive harmonic (DAH) decomposition approach for the spectral analysis and inverse modeling of multivariate time observations of complex nonlinear dynamical systems. DAH identifies frequency-based modes of interactions in the combined dataset of Auroral Electrojet (AE) index and solar wind forcing. The time evolution of these modes can be very effi- ciently simulated by using systems of stochastic differential equations (SDEs) that are stacked per frequency and formed by coupled Stuart-Landau oscillators. These systems of SDEs capture the modes’ frequencies as well as their amplitude modulations, and yield, in turn, an accurate modeling of the AE index’ statistical properties.

 

Chekroun, Mickaël D., Axel Kröner, and Honghu Liu. 2018. “Galerkin approximations for the optimal control of nonlinear delay differential equations.” Hamilton-Jacobi-Bellman Equations. Numerical Methods and Applications in Optimal Control, D. Kalise, K. Kunisch, and Z. Rao, 21: 61-96. Berlin, Boston: De Gruyter. Publisher's version Abstract

Optimal control problems of nonlinear delay  equations (DDEs) are considered for which we propose a general Galerkin approximation scheme built from Koornwinder polynomials. Error estimates for the resulting Galerkin-Koornwinder approximations to the optimal control and the value function, are derived for a broad class of cost functionals and nonlinear DDEs. The approach is illustrated on a delayed logistic equation set not far away from its Hopf bifurcation point in the parameter space. In this case, we show that low-dimensional controls for a standard quadratic cost functional can be efficiently computed from Galerkin-Koornwinder approximations to reduce at a nearly optimal cost the oscillation amplitude displayed by the DDE's solution. Optimal controls computed from the Pontryagin's maximum principle (PMP) and the Hamilton-Jacobi-Bellman equation (HJB) associated with the corresponding ODE systems, are shown to provide numerical solutions in good agreement. It is finally argued that the value function computed from the  corresponding reduced HJB equation provides a good approximation of that obtained from the full HJB equation.

Value function solving the reduced HJB equation

Cao, Yining, Mickaël D. Chekroun, Roger Temam, and Aimin Huang. In Press. “Mathematical analysis of the Jin-Neelin model of El Nino-Southern-Oscillation.” Chinese Annals of Mathematics, Series B. Abstract

The Jin-Neelin model for the El Nino-Southern Oscillation (ENSO) is considered for which we establish existence and uniqueness of global solutions in time over an unbounded channel domain. The result is proved for initial data and forcing that are sufficiently small. The smallness conditions involve in particular key physical parameters of the model such as those that control the travel time of the equatorial waves and the strength of feedbacks due to vertical-shear currents and upwelling; central mechanisms in ENSO dynamics.

From the mathematical view point, the system appears as the coupling of a linear shallow water system and a nonlinear heat equation. Because of the very different nature of the two components of the system, we find it convenient to prove the existence of solution by semi-discretization in time and utilization of a fractional step scheme (splitting method). The main idea consists of handling the coupling between the oceanic and temperature components by dividing the time interval into small sub-intervals of length $k$ and on each sub-interval to solve successively the oceanic component, using the temperature $T$ calculated on the previous sub-interval, to then solve the sea-surface temperature (SST) equation  on the current sub-interval. The passage to the limit as $k$ tends to zero is ensured via a priori estimates derived under the aforementioned smallness conditions.

Tantet, Alexis, Mickaël D. Chekroun, Henk A. Dijkstra, and J. David Neelin. Submitted. “Mixing Spectrum in Reduced Phase Spaces of Stochastic Differential Equations. Part II: Stochastic Hopf Bifurcation”. ArXiv's Version Abstract

The spectrum of the Markov semigroup of a diffusion process, referred to as the mixing spectrum, provides a detailed characterization of the dynamics of statistics such as the correlation function and the power spectrum. Stochastic analysis techniques for the study of the mixing spectrum and a rigorous reduction method have been presented in the first part (Chekroun et al. 2017) of this contribution. This framework is now applied to the study of a stochastic Hopf bifurcation, to characterize the statistical properties of nonlinear oscillators perturbed by noise, depending on their stability. In light of the H\"ormander theorem, it is first shown that the geometry of the unperturbed limit cycle, in particular its isochrons, i.e., the leaves of the stable manifold of the limit cycle generalizing the notion of phase, is essential to understand the effect of the noise and the phenomenon of phase diffusion. In addition, it is shown that the mixing spectrum has a spectral gap, even at the bifurcation point, and that correlations decay exponentially fast. Small-noise expansions of the eigenvalues and eigenfunctions are then obtained, away from the bifurcation point, based on the knowledge of the linearized deterministic dynamics and the characteristics of the noise. These formulas allow to understand how the interaction of the noise with the deterministic dynamics affect the decay of correlations. Numerical results complement the study of the mixing spectrum at the bifurcation point, revealing interesting scaling laws. The analysis of the Markov semigroup for stochastic bifurcations is thus promising in providing a complementary approach to the more geometric random dynamical systems. This approach is not limited to low-dimensional systems and the reduction method presented in part I will be applied to stochastic models relevant to climate dynamics in Part III.

Kondrashov, Dmitri, Mickaël D. Chekroun, and Pavel Berloff. 2018. “Multiscale Stuart-Landau Emulators: Application to Wind-Driven Ocean Gyres.” Fluids 3 (1): 21. Publisher's Version Abstract

The multiscale variability of the ocean circulation due to its nonlinear dynamics remains a big challenge for theoretical understanding and practical ocean modeling. This paper demonstrates how the data-adaptive harmonic (DAH) decomposition and inverse stochastic modeling techniques introduced in (Chekroun and Kondrashov, (2017), Chaos, 27), allow for reproducing with high fidelity the main statistical properties of multiscale variability in a coarse-grained eddy-resolving ocean flow. This fully-data-driven approach relies on extraction of frequency-ranked time-dependent coefficients describing the evolution of spatio-temporal DAH modes (DAHMs) in the oceanic flow data. In turn, the time series of these coefficients are efficiently modeled by a family of low-order stochastic differential equations (SDEs) stacked per frequency, involving a fixed set of predictor functions and a small number of model coefficients. These SDEs take the form of stochastic oscillators, identified as multilayer Stuart–Landau models (MSLMs), and their use is justified by relying on the theory of Ruelle–Pollicott resonances. The good modeling skills shown by the resulting DAH-MSLM emulators demonstrates the feasibility of using a network of stochastic oscillators for the modeling of geophysical turbulence. In a certain sense, the original quasiperiodic Landau view of turbulence, with the amendment of the inclusion of stochasticity, may be well suited to describe turbulence.

Decadal DAH mode

Kondrashov, Dmitri, Mickaël D. Chekroun, and Michael Ghil. 2018. “Data-adaptive harmonic decomposition and prediction of Arctic sea ice extent.” Dynamics and Statistics of the Climate System 3 (1): 1. Publisher's Version Abstract

Decline in the Arctic sea ice extent (SIE) is an area of active scientific research with profound socio-economic implications. Of particular interest are reliable methods for SIE forecasting on subseasonal time scales, in particular from early summer into fall, when sea ice coverage in the Arctic reaches its minimum. Here, we apply the recent data-adaptive harmonic (DAH) technique of Chekroun and Kondrashov, (2017), Chaos, 27 for the description, modeling and prediction of the Multisensor Analyzed Sea Ice Extent (MASIE, 2006–2016) data set. The DAH decomposition of MASIE identifies narrowband, spatio-temporal data-adaptive modes over four key Arctic regions. The time evolution of the DAH coefficients of these modes can be modelled and predicted by using a set of coupled Stuart–Landau stochastic differential equations that capture the modes’ frequencies and amplitude modulation in time. Retrospective forecasts show that our resulting multilayer Stuart–Landau model (MSLM) is quite skilful in predicting September SIE compared to year-to-year persistence; moreover, the DAH–MSLM approach provided accurate real-time prediction that was highly competitive for the 2016–2017 Sea Ice Outlook.

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