Kuramoto Sivashinsky Research Articles

Two-level implicit high order method based on half-step discretization for 1D unsteady biharmonic problems of first kind

In this work, compact difference scheme based on half-step discretization is proposed to solve fourth order time dependent partial differential equations subject to Dirichlet and Neumann boundary conditions. The difference method reported here is second order accurate in time and fourth order accurate in space. The scheme employs only three grid points at each time-level and the given boundary conditions are exactly satisfied with no further approximations at the boundaries. The linear stability of the presented method has been examined and it is shown that the proposed two-level finite difference method is unconditionally stable for a model linear problem. The developed method is directly applicable to fourth order parabolic partial differential equations with singular coefficients which is the main highlight of our work. The method is successfully tested on singular problem. The proposed method is applied to find the numerical solution of the classical nonlinear Kuramoto–Sivashinsky equation and the extended Fisher–Kolmogorov equation. Comparison of the obtained results with those of some earlier known methods demonstrate the superiority of the present approach.

Testing efficiency of the generalised $$\left( {{{G}'}/G} \right) $$ G ′ / G -expansion method for solving nonlinear evolution equations

In this investigation, we employ the generalised $$(G'{/}G)$$ -expansion method to test its efficiency in extracting travelling wave solutions of nonlinear evolution equations (NLEEs). As test cases, the modified Kuramoto–Sivashinsky (mK-S) and the modified Burgers–Korteweg–de Vries (mB-KdV) equations are considered because of their importance in soliton theory. The general solutions are obtained in hyperbolic, trigonometric and rational function forms for both the equations. Taking specific parametric values in the corresponding general solutions, some new exact travelling waves in trigonometric and hyperbolic forms and only in hyperbolic form are obtained for the mK-S and mB-KdV equations, respectively. The obtained results are checked to see whether the criticism made by Parkes (Comput. Fluids 42, 108 (2011)), that the so-called ‘new’ solutions derived by the $$(G'{/}G)$$ -expansion method are often erroneous and are merely disguised versions of previously known solutions, is justified also for the generalised $$(G'{/}G)$$ -expansion method. The solutions were checked with Maple by putting them back into their corresponding equations. With specific values of parameters, some of our obtained solutions satisfied directly and some solutions never satisfied the considered NLEEs. Among the satisfactory solutions, some are found to be in disguised versions of some results obtained in this study.

On the consistency of the local ensemble square root Kalman filter perturbation update

We examine the perturbation update step of the ensemble Kalman filters which rely on covariance localisation, and hence have the ability to assimilate non-local observations in geophysical models. We show that the updated perturbations of these ensemble filters are not to be identified with the main empirical orthogonal functions of the analysis covariance matrix, in contrast with the updated perturbations of the local ensemble transform Kalman filter (LETKF). Building on that evidence, we propose a new scheme to update the perturbations of a local ensemble square root Kalman filter (LEnSRF) with the goal to minimise the discrepancy between the analysis covariances and the sample covariances regularised by covariance localisation. The scheme has the potential to be more consistent and to generate updated members closer to the model’s attractor (showing fewer imbalances). We show how to solve the corresponding optimisation problem and discuss its numerical complexity. The qualitative properties of the perturbations generated from this new scheme are illustrated using a simple one-dimensional covariance model. Moreover, we demonstrate on the discrete Lorenz–96 and continuous Kuramoto–Sivashinsky one-dimensional low-order models that the new scheme requires significantly less, and possibly none, multiplicative inflation needed to counteract imbalance, compared to the LETKF and the LEnSRF without the new scheme. Finally, we notice a gain in accuracy of the new LEnSRF as measured by the analysis and forecast root mean square errors, despite using well-tuned configurations where such gain is very difficult to obtain.

Two scenarios of advective washing-out of localized convective patterns under frozen parametric disorder

The effect of spatial localization of states in distributed parameter systems under frozen parametric disorder is well known as the Anderson localization and thoroughly studied for the Schrödinger equation and linear dissipation-free wave equations. Some similar (or mimicking) phenomena can occur in dissipative systems such as the thermal convection ones. Specifically, many of these dissipative systems are governed by a modified Kuramoto–Sivashinsky equation, where the frozen spatial disorder of parameters has been reported to lead to excitation of localized patterns. Imposed advection in the modified Kuramoto–Sivashinsky equation can affect the localized patterns in a non-trivial way; it changes the localization properties and suppresses the pattern. The latter effect is considered in this paper by means of both numerical simulation and model reduction, which turns out to be useful for a comprehensive understanding of the bifurcation scenarios in the system. Two possible bifurcation scenarios of advective suppression (‘washing-out’) of localized patterns are revealed and characterized.

The Kuramoto–Sivashinsky Equation. A Local Attractor Filled with Unstable Periodic Solutions

A periodic boundary value problem is considered for one of the first versions of the Kuramoto–Sivashinsky equation, which is widely known in mathematical physics. Local bifurcations in a neighborhood of spatially homogeneous equilibrium points are studied in the case when they change stability. It is shown that the loss of stability of homogeneous equilibrium points leads to the occurrence of a two-dimensional local attractor on which all solutions are periodic functions of time, except for one spatially inhomogeneous state. The spectrum of frequencies of this family of periodic solutions fills the entire number line, and all of them are unstable in the sense of Lyapunov’s definition in the metric of the phase space (the space of initial conditions) of the corresponding initial boundary value problem. As the phase space, a Sobolev functional space natural for this boundary value problem is chosen. Asymptotic formulas are given for periodic solutions filling the two-dimensional attractor. To analyze the bifurcation problem, analysis methods for an infinite-dimensional dynamical system are used: the integral (invariant) manifold method combined with the methods of the Poincare normal form theory and asymptotic methods. Analyzing the bifurcations for the periodic boundary value problem is reduced to analyzing the structure of the neighborhood of the zero solution to the homogeneous Dirichlet boundary value problem for the equation under consideration.

Re-modified quintic B-spline collocation method for the solution of Kuramoto–Sivashinsky type equations

PurposeThe purpose of this paper is to present a new method, namely, “Re-modified quintic B-spline collocation method” to solve the Kuramoto–Sivashinsky (KS) type equations. In this method, re-modified quintic B-spline functions and the Crank–Nicolson formulation is used for space and time integration, respectively. Five examples are considered to test out the efficiency and accuracy of the method. The main objective is to develop a method which gives more accurate results and reduces the computational cost so that the authors require less memory storage.Design/methodology/approachA new collocation technique is developed to solve the KS type equations. In this technique, quintic B-spline basis functions are re-modified and used to integrate the space derivatives while time derivative is discretized by using Crank–Nicolson formulation. The discretization yields systems of linear equations, which are solved by using Gauss elimination method with partial pivoting.FindingsFive examples are considered to test out the efficiency and accuracy of the method. Finally, the present study summarizes the following outcomes: first, the computational cost of the proposed method is the less than quintic B-spline collocation method. Second, the present method produces better results than those obtained by Lattice Boltzmann method (Lai and Ma, 2009), quintic B-spline collocation method (Mittal and Arora, 2010), quintic B-spline differential quadrature method (DQM) (Mittal and Dahiya, 2017), extended modified cubic B-spline DQM (Tamsir et al., 2016) and modified cubic B-splines collocation method (Mittal and Jain, 2012).Originality/valueThe method presented in this paper is new to best of the authors’ knowledge. This work is the original work of authors and the manuscript is not submitted anywhere else for publication.

Stackelberg–Nash exact controllability for the Kuramoto–Sivashinsky equation

This paper deals with a hierarchical control problem for the Kuramoto–Sivashinsky equation following a Stackelberg–Nash strategy. We assume that there is a main control, called the leader, and two secondary controls, called the followers. The leader tries to drive the solution to a prescribed target and the followers intend to be a Nash equilibrium for given functionals. It is known that this problem is equivalent to a null controllability result for an optimality system consisting of three non-linear equations. One of the novelties is a new Carleman estimate for a fourth-order equation with right-hand sides in Sobolev spaces of negative order, which allows to relax some geometric conditions for the observation sets for the followers.

Modified Kudryashov Method to Solve Generalized Kuramoto-Sivashinsky Equation

The generalized Kuramoto–Sivashinsky equation is investigated using the modified Kudryashov method for the new exact solutions. The modified Kudryashov method converts the given nonlinear partial differential equation to algebraic equations, as a result of various steps, which upon solving the so-obtained equation systems yields the analytical solution. By this way, various exact solutions including complex structures are found, and their behavior is drawn in the 2D plane by Maple to compare the uniqueness and wave traveling of the solutions.

Fronts described by the Kuramoto–Sivashinsky equation under surface tension driven flow

We study chemical reaction fronts described by the Kuramoto–Sivashinsky equation coupled to surface tension driven flow. We consider the front as a thin interface separating reacted from unreacted fluids inside a two-dimensional fluid layer. The top surface of the layer is open to the atmosphere. Fluid motion is caused by surface tension gradients across the reaction front. We use the equations describing Stokes flow to model the fluid motion inside the fluid layer. We apply a linear stability analysis to determine the stability of the steady front solutions. We find that initially unstable fronts can become stable due to fluid motion.

A fourth‐order H1‐Galerkin mixed finite element method for Kuramoto–Sivashinsky equation

A H1‐Galerkin mixed finite element method is applied to the Kuramoto–Sivashinsky equation by using a splitting technique, which results in a coupled system. The method described in this article may also be considered as a Petrov–Galerkin method with cubic spline space as trial space and piecewise linear space as test space, since the second derivative of a cubic spline is a linear spline. Optimal‐order error estimates are obtained without any restriction on the mesh for both semi‐discrete and fully discrete schemes. The advantage of this method over that presented in Manickam et al., Comput. Math. Appl. vol. 35(6) (1998) pp. 5–25; for the same problem is that the size (i.e., (n + 1) × (n + 1)) of each resulting linear system is less than half of the size of the linear system of the earlier method, where n is the number of subintervals in the partition. Further, there is a requirement of less regularity on exact solution in this method. The results are validated with numerical examples. Finally, instability behavior of the solution is numerically captured with this method.

The Most Effective Model for Describing the Universal Behavior of a Noisy Kuramoto–Sivashinsky Equation as a Paradigmatic Model

We study a noisy Kuramoto–Sivashinsky (KS) equation which describes unstable surface growth and chemical turbulence. It has been conjectured that the universal long-wavelength behavior of the equation, which is characterized by scale-dependent parameters, is described by a Kardar–Parisi–Zhang (KPZ) equation. We consider this conjecture by analyzing a renormalization-group equation for a class of generalized KPZ equations. We then uniquely determine the parameter values of the KPZ equation that most effectively describes the universal long-wavelength behavior of the noisy KS equation.

Ensemble variational assimilation as a probabilistic estimator – Part 1: The linear and weak non-linear case

Abstract. Data assimilation is considered as a problem in Bayesian estimation, viz. determine the probability distribution for the state of the observed system, conditioned by the available data. In the linear and additive Gaussian case, a Monte Carlo sample of the Bayesian probability distribution (which is Gaussian and known explicitly) can be obtained by a simple procedure: perturb the data according to the probability distribution of their own errors, and perform an assimilation on the perturbed data. The performance of that approach, called here ensemble variational assimilation (EnsVAR), also known as ensemble of data assimilations (EDA), is studied in this two-part paper on the non-linear low-dimensional Lorenz-96 chaotic system, with the assimilation being performed by the standard variational procedure. In this first part, EnsVAR is implemented first, for reference, in a linear and Gaussian case, and then in a weakly non-linear case (assimilation over 5 days of the system). The performances of the algorithm, considered either as a probabilistic or a deterministic estimator, are very similar in the two cases. Additional comparison shows that the performance of EnsVAR is better, both in the assimilation and forecast phases, than that of standard algorithms for the ensemble Kalman filter (EnKF) and particle filter (PF), although at a higher cost. Globally similar results are obtained with the Kuramoto–Sivashinsky (K–S) equation.

Generalization of Gegenbauer Wavelet Collocation Method to the Generalized Kuramoto–Sivashinsky Equation

Gegenbauer (Ultraspherical) wavelets operational matrices play an important role for numeric solution of differential equations. In this study, operational matrices of rth integration of Gegenbauer wavelets are presented and general procedures of these matrices are correspondingly given first time. The proposed method is based on the approximation by the truncated Gegenbauer wavelet series. Algebraic equation system has been obtained by using the Chebyshev collocation points and solved. Proposed method has been applied to the Generalized Kuramoto–Sivashinsky equation using quasilinearization technique. Numerical examples showed that the method proposed in this study demonstrates the applicability and the accuracy of the Gegenbauer wavelet collocation method.

Oscillatory instability in a reaction front separating fluids of different densities.

Reaction fronts described by the Kuramoto-Sivashinsky (KS) equation can exhibit complex behavior as they separate reacted from unreacted fluids. If the fluid of higher density is above a fluid of lower density, then the Rayleigh-Taylor instability can lead to fluid motion. In the reverse situation, where the lighter fluid is on top, gravitationally driven forces can stabilize a convectionless flat front inhibiting the complex front propagation described by the KS equation. In these cases, a critical density difference is required to provide stability to the flat front. A linear stability analysis shows that the transition from stable to unstable flat fronts can be oscillatory for viscous fluid motion. Once the transition takes place, the fronts exhibit oscillatory convection resulting in oscillations of the shape and speed of the front.

Distributed sampled-data control of Kuramoto–Sivashinsky equation

Abstract The paper is devoted to distributed sampled-data control of nonlinear PDE system governed by 1-D Kuramoto–Sivashinsky equation. It is assumed that N sensors provide sampled in time spatially distributed (either point or averaged) measurements of the state over N sampling spatial intervals. Locally stabilizing sampled-data controllers are designed that are applied through distributed in space shape functions and zero-order hold devices. Given upper bounds on the sampling intervals in time and in space, sufficient conditions ensuring regional exponential stability of the closed-loop system are established in terms of Linear Matrix Inequalities (LMIs) by using the time-delay approach to sampled-data control and Lyapunov–Krasovskii method. As it happened in the case of diffusion equation, the descriptor method appeared to be an efficient tool for the stability analysis of the sampled-data Kuramoto–Sivashinsky equation. An estimate on the domain of attraction is also given. A numerical example demonstrates the efficiency of the results.

Topological classification of periodic orbits in the Kuramoto–Sivashinsky equation

In this paper, we systematically research periodic orbits of the Kuramoto–Sivashinsky equation (KSe). In order to overcome the difficulties in the establishment of one-dimensional symbolic dynamics in the nonlinear system, two basic periodic orbits can be used as basic building blocks to initialize cycle searching, and we use the variational method to numerically determine all the periodic orbits under parameter [Formula: see text] = 0.02991. The symbolic dynamics based on trajectory topology are very successful for classifying all short periodic orbits in the KSe. The current research can be conveniently adapted to the identification and classification of periodic orbits in other chaotic systems.

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

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Optimal neural network feature selection for spatial-temporal forecasting.

Neural networks, and in general machine learning techniques, have been widely employed in forecasting time series and more recently in predicting spatial-temporal signals. All of these approaches involve some kind of feature selection regarding what past data and what neighbor data to use for forecasting. In this article, we show extensive empirical evidence on how to independently construct the optimal feature selection or input representation used by the input layer of a feed forward neural network for the purpose of forecasting spatial-temporal signals. The approach is based on results from the dynamical systems theory, namely, nonlinear embedding theorems. We demonstrate it for a variety of spatial-temporal signals and show that the optimal input layer representation consists of a grid, with spatial-temporal lags determined by the minimum of the mutual information of the spatial-temporal signals and the number of points taken in space-time decided by the embedding dimension of the signal. We present evidence of this proposal by running a Monte Carlo simulation of several combinations of input layer feature designs and show that the one predicted by the nonlinear embedding theorems seems to be optimal or close to being optimal. In total, we show evidence in four unrelated systems: a series of coupled Hénon maps, a series of coupled ordinary differential equations (Lorenz-96) phenomenologically modeling atmospheric dynamics, the Kuramoto-Sivashinsky equation, a partial differential equation used in studies of instabilities in laminar flame fronts, and finally real physical data from sunspot areas in the Sun (in latitude and time) from 1874 to 2015. These four examples cover the range from simple toy models to complex nonlinear dynamical simulations and real data. Finally, we also compare our proposal against alternative feature selection methods and show that it also works for other machine learning forecasting models.

Global Carleman estimates for the linear stochastic Kuramoto–Sivashinsky equations and their applications

In this paper, we establish two new global Carleman estimates for the linear stochastic Kuramoto–Sivashinsky equations. The first one is for the backward linear stochastic Kuramoto–Sivashinsky equation. Based on this estimate and the duality argument, we obtain the null controllability of the forward linear stochastic Kuramoto–Sivashinsky equation by three controls, one is an internal control in the diffusion term and the others are boundary controls. The second one is for the forward linear stochastic Kuramoto–Sivashinsky equation. According to this estimate, we obtain a unique continuation property for the forward linear stochastic Kuramoto–Sivashinsky equation.

Global Existence and Analyticity for the 2D Kuramoto–Sivashinsky Equation

There is little analytical theory for the behavior of solutions of the Kuramoto–Sivashinsky equation in two spatial dimensions over long times. We study the case in which the spatial domain is a two-dimensional torus. In this case, the linearized behavior depends on the size of the torus—in particular, for different sizes of the domain, there are different numbers of linearly growing modes. We prove that small solutions exist for all time if there are no linearly growing modes, proving also in this case that the radius of analyticity of solutions grows linearly in time. In the general case (i.e., in the presence of a finite number of growing modes), we make estimates for how the radius of analyticity of solutions changes in time.