Set Of Stochastic Differential Equations Research Articles

Polarization evolution in Brillouin scattering with a partially polarized pump

Polarization sensitivity has been a major issue in Brillouin scattering-based optical fiber sensing systems. Randomization of the polarization state of the pump is one of the ways to circumvent the problem. However, there could exist a residual degree of polarization (DOP) for the pump after polarization randomization, and hence, a model to characterize the polarization evolution in Brillouin scattering with a partially polarized pump is essential for the performance evaluation. In this work, a comprehensive theoretical model to characterize the beam variation with the partially polarized pump wave and Stokes wave is proposed, which is based on a set of stochastic differential equations (SDEs). The polarized part of the pump wave and the Stokes wave, as well as the total powers of the waves, are incorporated in the coupled SDE simultaneously, which enables the comprehensive simulation of the polarization evolution in the fiber. It is revealed in the study that the DOPs of the pump wave and the Stokes wave affect the gain stability and should be reduced simultaneously by polarization scrambling to ensure a fixed Brillouin gain without fluctuations.

Global attracting set of stochastic differential equations with unbounded delay driven by fractional Ornstein–Uhlenbeck process

Abstract In this paper, we have studied stochastic differential equations with unbounded delay in fractional power spaces perturbed by fractional Ornstein–Uhlenbeck process Y H , ξ ⁢ ( t ) {{Y^{H,\xi}}(t)} with H ∈ ( 1 2 , 1 ) {H\in(\frac{1}{2},1)} . Subsequently, the existence and uniqueness of mild solution of the considered equation have been proved with fixed-point theorem. Finally, we obtain the global attracting set of the considered equations by some stochastic analysis and inequality technique.

Coupled Price–Volume Equity Models with Auto-Induced Regime Switching

In this work, we present a rigorous development of a model for the Price–Volume relationship of transactions introduced in 2009. For this development, we rely on the precise formulation of diffusion auto-induced regime-switching models presented in our previous work of 2020. The auto-induced regime-switching models referred to may be based on a finite set of stochastic differential equations (SDE)—all defined on the same bounded time interval—and a sequence of interlacing stopping times defined by the hitting threshold times of the trajectories of the solutions of the SDE. The coupling between price and volume—which we take as a proxy of liquidity—is assumed to be the following: the regime switching in the price variable occurs at the stopping times for which there is a change of region—in the product state space of price and liquidity—for the liquidity variable (and vice versa). The regimes may be defined parametrically—that is, the SDE coefficients keep the same functional form but with varying parameters—or the functional form of the SDE coefficients may change with each regime. By using the same noise source for both the price and the liquidity regime-switching models—volume (liquidity), which, in general, is not a tradable asset—we ensure that despite incorporating information on liquidity, the price part of the coupled model can be assumed to be arbitrage free and complete, allowing the pricing and hedging of derivatives in a simple way.

Lévy flights as an emergent phenomenon in a spatially extended system

Anomalous diffusion and Lévy flights, which are characterized by the occurrence of random discrete jumps of all scales, have been observed in a plethora of natural and engineered systems, ranging from the motion of molecules to climate signals. Mathematicians have recently unveiled mechanisms to generate anomalous diffusion, both stochastically and deterministically. However, there exists to the best of our knowledge no explicit example of a spatially extended system which exhibits anomalous diffusion without being explicitly driven by Lévy noise. We show here that the Landau–Lifshitz–Gilbert equation, a stochastic partial differential equation (SPDE), despite only being driven by Gaussian white noise, exhibits superdiffusive behaviour. The anomalous diffusion is an entirely emergent behaviour and manifests itself in jumps in the location of its travelling front solution. Using a collective coordinate approach, we reduce the SPDE to a set of stochastic differential equations driven by Gaussian white noise. This allows us to identify the mechanism giving rise to the anomalous diffusion as random widening events of the front interface.

Lagrangian Stochastic Model for the Motions of Magnetic Footpoints on the Solar Wind Source Surface and the Path Lengths of Boundary-driven Interplanetary Magnetic Field Lines

In this work, we extend Leighton’s diffusion model describing the turbulent mixing of magnetic footpoints on the solar wind source surface. The present Lagrangian stochastic model is based on the spherical Ornstein–Uhlenbeck process with drift that is controlled by the rotation frequency Ω of the Sun, the Lagrangian integral timescale τ L, and the root-mean-square footpoint velocity V rms. The Lagrangian velocity and the positions of magnetic footpoints on the solar wind source surface are obtained from the solutions of a set of stochastic differential equations, which are solved numerically. The spherical diffusion model of Leighton is recovered in the singular Markov limit when the Lagrangian integral timescale tends to zero while keeping the footpoint diffusivity finite. In contrast to the magnetic field lines driven by standard Brownian processes on the solar wind source surface, the interplanetary magnetic field lines are smooth differentiable functions with finite path lengths in our model. The path lengths of the boundary-driven interplanetary magnetic field lines and their probability distributions at 1 au are computed numerically, and their dependency with respect to the controlling parameters is investigated. The path-length distributions are shown to develop a significant skewness as the width of the distributions increases.

State and Parameter Estimation of Non-Uniform Transmission Line Using Kronecker Product Based Modeling

The distributed parameters of a non-uniform transmission line (NTL) are assumed to depend on a finite set of unknown parameters, for example, their spatial Fourier series coefficients. The line equations are set up taking random voltage and current line loading into account with the random loading being assumed to be white Gaussian noise in the time and spatial variables. Bypassing over to the spatial Fourier series domain, these line stochastic differential equations (SDEs) are expressed as a truncated sequence of coupled stochastic differential equations for the spatial Fourier series components of the line voltage and current. The coefficients that appear in these stochastic differential equations are the spatial Fourier series coefficients of the distributed line parameters and these coefficients in turn are linear functions of the unknown parameters. By separating these complex stochastic differential equations into their real and imaginary part, the line equations become a finite set of SDEs for the extended state defined to be the real and imaginary components of the line voltage and current as well as the parameters on which the distributed parameters depend. The measurement model is assumed to be white Gaussian noise corrupted versions of the line voltage and current sampled at a discrete set of spatial points on the line and this measurement model can be expressed as a linear transformation of the state variables, i.e, of the spatial Fourier series components of the line voltage and current plus a white measurement noise vector. This work uses sparse matrix factorization using Kronecker product to implement the Fourier unitary transform. This work implements perturbation theory for non-uniform transmission line. The extended Kalman filter (EKF) equations are derived for this state and measurement model and our real timeline voltage and current and distributed parameter estimates show excellent agreement with the true values based on MATLAB simulations. The use of sparse matrix factorization using Kronecker product ease the mathematical complexity and provides compact representation.

The effect and properties of drifts in the heliosphere

We investigate the properties of drifts and their effect on cosmic ray modulation in the heliosphere using a numerical modulation model based on the solution of a set of stochastic differential equations that was derived from the Parker transport equation. The illustrative capabilities of the numerical model are exploited to yield a better understanding of the physical modulation processes involved. Various studies have indicated that drifts need to be scaled down towards solar maximum conditions and the present study looks at how this can be achieved. Drifts are scaled down directly by multiplying the drift coefficient by a factor of less than unity as well as indirectly through the drift–diffusion relation, that is, by modifying the diffusion coefficient so as to cause a change in the drift effects through altered gradients in particle intensity. Contour plots of particle exit positions and exit energies are presented for both of these cases, and it is illustrated that drifts in the model lead to larger energy losses. This is explained with the aid of figures indicating the relative amount of time spent by pseudo-particles in different regions of the heliosphere during the modulation process. These figures also indicate that an increase in diffusion leads to a suppression or reduction of drift effects. Finally, the figures also show that drift effects are reduced as a function of increasing particle energy; even though the drift coefficient increases with particle energy, the total drift effect, taking into account the contribution from the increased diffusion associated with larger energies, causes drift effects to be reduced with an increase in energy.

Time recursive control of stochastic dynamical systems using forward dynamics and applications

The solution of a stochastic optimal control problem may be associated with that of the Hamilton–Jacobi–Bellman (HJB) equation, which is a second order partial differential equation subject to a terminal condition. When this equation is semilinear and satisfies certain other constraints, it can be solved via a nonlinear version of the Feynman–Kac formula. According to this approach, the solution to the HJB equation can be obtained by simulating an associated pair of partly coupled forward–backward stochastic differential equations. Although an elegant way to interpret and solve a partial differential equation, simulating the system of forward–backward equations can be computationally inefficient. In this work, the HJB equation pertaining to the optimal control problem is reformulated such that instead of the given terminal condition, it is now subject to an appropriate initial condition. In the process, while the total cost associated with the control problem remains unchanged, pathwise solutions may not. Associated with the new partial differential equation, we then derive a set of stochastic differential equations whose solutions move only forward in time. This approach has a significant computational advantage over the original formulation. Moreover, since the forward–backward approach generally requires simulating stochastic differential equations over the current to the terminal time at every step, the integration errors may accumulate and carry forward in estimating the control. This error is particularly high initially since the time of integration is the longest there. The proposed method, numerically implemented for the control of stochastically excited oscillators, is free of such errors and hence more robust. Unsurprisingly, it also exhibits lower sampling variance.

Inferring temporal dynamics from cross-sectional data using Langevin dynamics.

Cross-sectional studies are widely prevalent since they are more feasible to conduct compared with longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying dynamics. This temporal information is essential to develop predictive computational models, which is the first step towards causal modelling. We propose a method for inferring computational models from cross-sectional data using Langevin dynamics. This method can be applied to any system where the data-points are influenced by equal forces and are in (local) equilibrium. The inferred model will be valid for the time span during which this set of forces remains unchanged. The result is a set of stochastic differential equations that capture the temporal dynamics, by assuming that groups of data-points are subject to the same free energy landscape and amount of noise. This is a ‘baseline’ method that initiates the development of computational models and can be iteratively enhanced through the inclusion of domain expert knowledge as demonstrated in our results. Our method shows significant predictive power when compared against two population-based longitudinal datasets. The proposed method can facilitate the use of cross-sectional datasets to obtain an initial estimate of the underlying dynamics of the respective systems.

Rouse model with fluctuating internal friction

A coarse-grained bead-spring-dashpot chain model with the dashpots representing the presence of internal friction is solved exactly numerically for the case of chains with more than two beads. Using a decoupling procedure to remove the explicit coupling of a bead’s velocity with that of its nearest neighbors, the governing set of stochastic differential equations are solved with Brownian dynamics simulations to obtain material functions in oscillatory and steady simple shear flow. Simulation results for the real and imaginary components of the complex viscosity have been compared with the results of previously derived semi-analytical approximations, and the difference in the predictions is seen to diminish with an increase in the number of beads in the chain. The inclusion of internal friction results in a nonmonotonous variation of the viscosity with shear rate, with the occurrence of continuous shear-thickening following an initial shear-thinning regime. The onset of shear-thickening in the first normal stress coefficient is pushed to lower shear rates with an increase in the internal friction parameter.

Quantum Kerr nonlinear coupler: analytical versus phase-space method

The generation of squeezed states of light in a two-mode Kerr nonlinear directional coupler (NLDC) was investigated using two different methods in quantum mechanics. First, the analytical method, a Heisenberg-picture-based method where the operators are evolving in time but the state vectors are time-independent. In this method, an analytical solution to the coupled Heisenberg equations of motion for the propagating modes was proposed based on the Baker–Hausdorff (BH) formula. Second, the phase space method, a Schrödinger-picture-based method in which the operators are constant and the density matrix evolves in time. In this method, the quantum mechanical master equation of the density matrix was converted to a corresponding classical Fokker–Planck (FP) equation in positive-P representation. Then, the FP equation was converted to a set of stochastic differential equations using Ito rules. The strengths and weaknesses of each method are discussed. Good agreement between both methods was achieved, especially at early evolution stages and lower values of linear coupling coefficient. On one hand, the analytical method seems insensitive to higher values of nonlinear coupling coefficients. Nevertheless, it demonstrated better numerical stability. On the other hand, the solution of the stochastic equations resulting from the phase space method is numerically expensive as it requires averaging over thousands of trajectories. Besides, numerically unstable trajectories appear with positive-P representation at higher values of nonlinearity.

Stochastic Parker Spirals in the Solar Wind

Abstract An analytic model for the angular dispersion of magnetic field lines resulting from the turbulence in the solar wind and at the solar source surface is presented. The heliospheric magnetic field lines in our model are derived from a Hamiltonian with the pair of canonically conjugated variables the cosine of the heliographic colatitude μ and the longitude ϕ. In the diffusion approximation, the Parker spirals are modeled by a set of stochastic differential equations for θ and ϕ as functions of r. These stochastic Parker spirals are realizations of a standard random walk on a sphere of increasing radius, superimposed on an angular drift due to solar rotation. The Green function solution of the Fokker–Planck equation describing the angular diffusion of the field line density is obtained in terms of spherical harmonics. Magnetic field lines traced from an observer back to the Sun are realizations of a Brownian bridge. Our model incorporates the effect of the random footpoint motions at the source surface, which is associated with the zero-frequency component of the solar wind turbulence. Assuming that the footpoint motion is diffusive, its contribution to the angular diffusivity of the stochastic Parker spirals is then given by the angular diffusivity of the footpoints divided by the solar wind speed and is controlled by a unique parameter, which is the Kubo number.

The effects of drift and winds on the propagation of Galactic cosmic rays

ABSTRACT We study the effects of drift motions and the advection by a Galactic wind on the propagation of cosmic rays in the Galaxy. We employ a simplified magnetic field model, based on (and similar to) the Jansson–Farrar model for the Galactic magnetic field. Diffusion is allowed to be anisotropic. The relevant equations are solved numerically, using a set of stochastic differential equations. Inclusion of drift and a Galactic wind significantly shortens the residence time of cosmic rays, even for moderate wind speeds.

Stochastic evolution of hydraulic geometry relations in the lower Yellow River of China under environmental uncertainties

Hydraulic geometry relations comprise a classic way to understand characteristics of a river. However, environmental changes pose large uncertainties for the reliability of such relations. In the current study, on the basis of the ordinary differential equations (ODEs) formed through linear treatment of the deterministic power-law hydraulic geometry relations, a set of stochastic differential equations (SDEs) driven by Fractional white noise and Poisson noise are developed to simulate the historical dynamic probability distributions of typical hydraulic geometry variables such as slope, width, depth, and velocity with bankfull discharge variation over time in the lower Yellow River of China. One group of possible stochastic average behaviors within the next 50 years are calculated under three different design incoming water-sediment conditions (including 300, 600, and 800 million t of annual average sediment discharge). In each part of the lower reaches, after estimation of the SDE parameters using a nonparametric maximum likelihood estimation (MLE) method, the model is carefully examined using Monte Carlo simulation as compared with the deterministic control models. The results of this comparison reveal the potential responses of hydraulic geometry characteristics to environmental disturbances, and the average trends mainly agree with the measurements. Comparisons among the three different prediction results reveal the stochastic average solution generally is greater than the deterministic solution. The results also confirm the severe negative impacts that result from the condition of 300 million t of incoming sediment, thus, pointing out the need to raise the level of river evolution alert for the lower Yellow River of China in the future. Moreover, with the help of the stochastic computation, the stream power and hydraulic width/depth ratio could be representative of an effective systematic measure for river dynamics. The proposed stochastic approach is not only important to development in the field of fluvial relations, but also beneficial to the practical design and monitoring of a river system according to specified accuracy requirements.

The contribution of nearby supernova remnants on the cosmic ray flux at Earth

ABSTRACT We consider anisotropic diffusion of Galactic cosmic rays in the Galactic magnetic field, using the Jansson–Farrar model for the field. In this paper, we investigate the influence of source position on the cosmic ray flux at the Earth in two ways: (1) by considering the contribution from cosmic ray sources located in different intervals in Galactocentric radius, and (2) by considering the contribution from a number of specific and individual close-by supernova remnants. Our calculation is performed by using a fully three-dimensional stochastic method. This method is based on the numerical solution of a set of stochastic differential equations, equivalent to Itô formulation, that describes the propagation of the Galactic cosmic rays.

Stochastic Small Signal Stability of a Power System with Uncertainties

The ever-increasing penetration of wind power generation and plug-in electric vehicles introduces stochastic continuous disturbances to the power system. This paper proposes an analytical approach to analyze the influence of stochastic continuous disturbances on power system small signal stability. The noise-to-state stability (NSS) and NSS Lyapunov function (NSS-LF) are adopted for stability analysis with respect to the magnitude of uncertainties in a power system. The power system is modeled as a set of stochastic differential equations (SDEs). The supremum of the norm of the covariance is employed to characterize the influence of magnitudes of uncertainties on the power system. Then the relationship between the magnitudes of stochastic variations and probabilistic stability is explicitly identified by NSS. The proposed method can assess the stochastic stability of the power system by checking some algebraic expressions. Hence, it has high computation efficiency compared with the well-established Monte Carlo based method. Besides, since the magnitudes of the stochastic variations are integrated into the definition of the stochastic stability, the proposed method provides theoretical explanations for the impacts of uncertainties.

Numerical integration of stochastic differential equations: A parallel cosmic ray modulation implementation on Africa’s fastest computer

Three-dimensional studies of the transport and modulation of cosmic ray particles in turbulent astrospheres require large-scale simulations using specialized scientific codes. Essentially, a multi-dimensional Fokker-Planck type equation (a parabolic diffusion equation) must be integrated numerically. One such approach is to convert the relevant transport equation into a set of stochastic differential equations (SDEs), with the latter much easier to handle numerically. Due to the growing demand for high performance computing resources, research into the application of effective and suitable numerical algorithms to solve such equations is needed. We present a case study of the performance of a custom-written FORTRAN SDE numerical solver on the CHPC (Centre for High Performance Computing) Lengau cluster in South Africa for a realistic test problem with different set-ups. It is shown that SDE codes can scale very well on large parallel computing platforms. Finally, we consider an extremely computationally expensive application of the SDE approach to cosmic ray modulation, studying the behaviour of galactic cosmic ray proton latitude gradients and relative amplitudes in a physics-first manner. This is done using a modulation code that employs diffusion coefficients derived from first principles, which in turn are functions of turbulence quantities in reasonable agreement with spacecraft observations and modelled using a two-component turbulence transport model (TTM). We show that this approach leads to reduced latitude gradients qualitatively in line with spacecraft observations of the same, without making ad hoc assumptions as to anisotropic perpendicular diffusion coefficients as are often made in many cosmic ray modulation studies.

Quantum dynamics of a four-channel Kerr nonlinear directional coupler

Quantum dynamics of a four-channel nonlinear directional coupler, based on Kerr effect, was modeled numerically by a set of stochastic differential equations derived using quasiprobability distribution of positive- $$P$$ representation. The modeling of the system is focused on the properties of quadrature evolution below the standard quantum limit, and its comparison with the conventional two-channel device. The results exhibit that a four-channel Kerr coupler provides an effective way to manipulate squeezing, especially in the mixed-mode basis, as compared with the conventional two-channel system—the theme that would make these to be prudent squeezed light source.

Stochastic Stability of a Class of MEMS-Based Vibratory Gyroscopes under Input Rate Fluctuations

The influence of stochastic fluctuations in the input angular rate of a class of single axis mass-spring microelectromechanical (MEM) gyroscopes on the system stability is investigated. A white noise fluctuation is introduced in the coupled 2-DOF model that represents the system dynamics for the purposes of stability prediction. Numerical simulations are performed employing the resulting set of stochastic differential equations (SDEs) that govern the system dynamics. The SDEs are discretized using the higher-order Milstein scheme for numerical computations. Simulations via the Euler scheme, as well as the measure of the largest Lyapunov exponent are employed for validation purposes due to a lack of similar analytical solutions or experimental data. Responses have been predicted under different noise fluctuation magnitudes and different input angular rates for stability investigations. A parametric study is performed to estimate the noise intensity stability threshold for a range of quality factor values at different input angular rates. The predicted results show a nonlinear dependence of the threshold on the quality factors for different input rates. Under typical gyroscope operating conditions, a realistic frequency mismatch appears to have insignificant influence on system stability. It is envisaged that the present quantitative predictions will aid improvements in performance, reliability, and the design process for this class of devices.

Application of the Ito theory for Monte Carlo simulation of plasma diffusion

In this paper the full set of stochastic differential equations (SDEs) are presented describing the guiding centre motion of test charged particles in a plasma with an arbitrary inhomogeneous magnetic field, when the drift approximation is applicable. The derivation is based on the Ito formula which is used to determine stochastic differentials of functions of the non-gyro-averaged velocity diffusion in strict correspondence with the general kinetic equations involving Coulomb collision operators. The drift SDEs are obtained by calculating the Ito stochastic integrals within time intervals admitting the gyro-averaging procedure. The proposed SDEs reproduce the well-known Monte Carlo operators for orbital invariants, however additionally accounting for the spatial drift caused by the cross-field diffusion process with a classical diffusion coefficient. All SDE coefficients are explicitly expressed in terms of the Rosenbluth potentials in a gyro-tropic or isotropic background plasma. The SDEs are presented in particular for the case of an axisymmetric toroidal magnetic configuration to describe the spatial two-dimensional poloidal diffusion process providing a detailed description of neoclassical orbital effects.