Wiener Noise Research Articles

Uniqueness for solutions of Fokker–Planck equations related to singular SPDE driven by Lévy and cylindrical Wiener noise

We generalize recent results concerning uniqueness of solutions to Fokker–Planck equations (FPE) related to singular Hilbert space-valued SPDE from the (cylindrical) Wiener noise case to the case of SPDE driven by noise with jumps. Using a different space of test functions, we can relax the usual integrability assumptions and obtain more general uniqueness results for FPE, even in the case of SPDE driven by Wiener noise.

Random Attractors for Degenerate Stochastic Partial Differential Equations

We prove the existence of random attractors for a large class of degenerate stochastic partial differential equations (SPDE) perturbed by joint additive Wiener noise and real, linear multiplicative Brownian noise, assuming only the standard assumptions of the variational approach to SPDE with compact embeddings in the associated Gelfand triple. This allows spatially much rougher noise than in known results. The approach is based on a construction of strictly stationary solutions to related strongly monotone SPDE. Applications include stochastic generalized porous media equations, stochastic generalized degenerate $$p$$ -Laplace equations and stochastic reaction diffusion equations. For perturbed, degenerate $$p$$ -Laplace equations we prove that the deterministic, $$\infty $$ -dimensional attractor collapses to a single random point if enough noise is added.

Existence and Regularity of the Density for Solutions to Semilinear Dissipative Parabolic SPDEs

We prove existence and smoothness of the density of the solution to a nonlinear stochastic heat equation on \(L^2(\mathcal{O})\) (evaluated at fixed points in time and space), where \(\mathcal{O}\) is an open bounded domain in ℝd with smooth boundary. The equation is driven by an additive Wiener noise and the nonlinear drift term is the superposition operator associated to a real function which is assumed to be (maximal) monotone, continuously differentiable, and growing not faster than a polynomial. The proof uses tools of the Malliavin calculus combined with methods coming from the theory of maximal monotone operators.

Ergodicity of Stochastic Burgers’ System with Dissipative Term

A 2-dimensional stochastic Burgers equation with dissipative term perturbed by Wiener noise is considered. The aim is to prove the well-posedness, existence, and uniqueness of invariant measure as well as strong law of large numbers and convergence to equilibrium.

Mean-square stability and error analysis of implicit time-stepping schemes for linear parabolic SPDEs with multiplicative Wiener noise in the first derivative

In this article, we extend a Milstein finite difference scheme introduced in 8 for a certain linear stochastic partial differential equation (SPDE) to semi-implicit and fully implicit time-stepping as introduced by Szpruch 32 for stochastic differential equations (SDEs). We combine standard finite difference Fourier analysis for partial differential equations with the linear stability analysis in 3 for SDEs to analyse the stability and accuracy. The results show that Crank–Nicolson time-stepping for the principal part of the drift with a partially implicit but negatively weighted double Itô integral gives unconditional stability over all parameter values and converges with the expected order in the mean-square sense. This opens up the possibility of local mesh refinement in the spatial domain, and we show experimentally that this can be beneficial in the presence of reduced regularity at boundaries.

Detection of continuous-time quaternion signals in additive noise

Different kinds of quaternion signal detection problems in continuous-time by using a widely linear processing are dealt with. The suggested solutions are based on an extension of the Karhunen-Loeve expansion to the quaternion domain which provides uncorrelated scalar real-valued random coefficients. This expansion presents the notable advantage of transforming the original four-dimensional eigen problem to a one-dimensional problem. Firstly, we address the problem of detecting a quaternion deterministic signal in quaternion Gaussian noise and a version of Pitcher’s Theorem is given. Also the particular case of a general quaternion Wiener noise is studied and an extension of the Cameron-Martin formula is presented. Finally, the problem of detecting a quaternion random signal in quaternion white Gaussian noise is tackled. In such a case, it is shown that the detector depends on the quaternion widely linear estimator of the signal.

Combined effects of correlated bounded noises and weak periodic signal input in the modified FitzHugh–Nagumo neural model

We study the dynamics of neurons via a bistable modified stochastic FitzHugh–Nagumo model having two stable fixed points separated by one unstable fixed point. Due to the ability of a neuron to detect and enhance weak information transmission, we show numerically that starting from the resting potential, we get firing activities (spiking) when operating slightly beyond the supercritical Hopf bifurcation. For real biological systems which are sometimes embedded in the complex environment, we observe that a gradual increase or decrease noise intensities did not result in a gradual change of the membrane potential distribution thanks to noise induced transition phenomena. We shown analytically that for zero correlation between two sine Wiener noises, additive noise has no effect on the transition between monostable and bistable phase on the neural model. We adapted a general expression of the signal-to-noise ratio for a general two-state theory extended in the asymmetric case and non-Gaussian noises in our model to study the influence of noise strength in stochastic resonance. Our investigation revealed that in the evolution of excitable system, neurons may use noises to their advantage by enhancing their sensitivity near a preferred phase to detect external stimuli or affect the efficiency and rate of information processing.

A pseudo-time EnKF incorporating shape based reconstruction for diffuse optical tomography

The authors aim at developing a pseudo-time, sub-optimal stochastic filtering approach based on a derivative free variant of the ensemble Kalman filter (EnKF) for solving the inverse problem of diffuse optical tomography (DOT) while making use of a shape based reconstruction strategy that enables representing a cross section of an inhomogeneous tumor boundary by a general closed curve. The optical parameter fields to be recovered are approximated via an expansion based on the circular harmonics (CH) (Fourier basis functions) and the EnKF is used to recover the coefficients in the expansion with both simulated and experimentally obtained photon fluence data on phantoms with inhomogeneous inclusions. The process and measurement equations in the pseudo-dynamic EnKF (PD-EnKF) presently yield a parsimonious representation of the filter variables, which consist of only the Fourier coefficients and the constant scalar parameter value within the inclusion. Using fictitious, low-intensity Wiener noise processes in suitably constructed "measurement" equations, the filter variables are treated as pseudo-stochastic processes so that their recovery within a stochastic filtering framework is made possible. In our numerical simulations, we have considered both elliptical inclusions (two inhomogeneities) and those with more complex shapes (such as an annular ring and a dumbbell) in 2-D objects which are cross-sections of a cylinder with background absorption and (reduced) scattering coefficient chosen as μ(a) (b)=0.01mm(-1) and μ(s) ('b)=1.0mm(-1), respectively. We also assume μ(a) = 0.02 mm(-1) within the inhomogeneity (for the single inhomogeneity case) and μ(a) = 0.02 and 0.03 mm(-1) (for the two inhomogeneities case). The reconstruction results by the PD-EnKF are shown to be consistently superior to those through a deterministic and explicitly regularized Gauss-Newton algorithm. We have also estimated the unknown μ(a) from experimentally gathered fluence data and verified the reconstruction by matching the experimental data with the computed one. The PD-EnKF, which exhibits little sensitivity against variations in the fictitiously introduced noise processes, is also proven to be accurate and robust in recovering a spatial map of the absorption coefficient from DOT data. With the help of shape based representation of the inhomogeneities and an appropriate scaling of the CH expansion coefficients representing the boundary, we have been able to recover inhomogeneities representative of the shape of malignancies in medical diagnostic imaging.

Mean-square stability analysis of numerical schemes for stochastic differential systems

For ordinary differential systems, the study of A-stability for a numerical method reduces to the scalar case by means of a transformation that uncouples the linear test system as well as the difference system provided by the method. For stochastic differential equations (SDEs), mean-square stability (MS-stability) has been successfully proposed as the generalization of A-stability, and numerical MS-stability has been analyzed for one-dimensional equations. However, unlike the deterministic case, the extension of this analysis to multi-dimensional systems is not straightforward. In this paper we give necessary and sufficient conditions for the MS-stability of multi-dimensional systems with one Wiener noise. The criterion presented does not depend on any norm. Based on the Routh–Hurwitz theorem, we offer a particular criterion of MS-stability for two-dimensional systems in terms of their coefficients. In addition, a counterpart criterion of MS-stability is given for numerical schemes applied to multi-dimensional systems. The MS-stability behavior of a stochastic numerical method is determined by the comparison of its stability region with the stability region of the system. As an application, the numerical MS-stability of θ-methods applied to bi-dimensional systems is investigated.

Robust Adaptive Switching Control for Markovian Jump NonlinearSystems via Backstepping Technique

This paper investigates robust adaptive switching controller design for Markovian jump nonlinear systems with unmodeled dynamics and Wiener noise. The concerned system is of strict‐feedback form, and the statistics information of noise is unknown due to practical limitation. With the ordinary input‐to‐state stability (ISS) extended to jump case, stochastic Lyapunov stability criterion is proposed. By using backstepping technique and stochastic small‐gain theorem, a switching controller is designed such that stochastic stability is ensured. Also system states will converge to an attractive region whose radius can be made as small as possible with appropriate control parameters chosen. A simulation example illustrates the validity of this method.

Optimal Control Approach to Nonlinear Diffusion Equations Driven by Wiener Noise

The stochastic nonlinear infinite-dimensional equations of gradient type and with additive Wiener noise can be reduced to an optimal convex control problem via Brezis–Ekeland duality device. This approach is illustrated here on a few classes of nonlinear stochastic parabolic equations which are relevant as diffusion models under stochastic Gaussian perturbations, and image restoring technique.

Adaptive tracking of stochastic nonlinear systems with Prandtl–Ishlinskii hysteresis

SUMMARYThis paper deals with adaptive tracking problems for a class of stochastic nonlinear systems with unknown hysteresis nonlinearities. The system considered is in a strict‐feedback form driven by unknown Prandtl–Ishlinskii hysteresis and Wiener noises of unknown covariance. By employing backstepping design techniques and stochastic Lyapunov design method, parameter adaptive laws and control laws are obtained, which ensure that the tracking error can converge to a small residual set around the origin in the sense of mean quartic value. Copyright © 2011 John Wiley & Sons, Ltd.

Jump diffusion models and the evolution of financial prices

We analyze a stochastic model to describe the evolution of financial prices. We consider the stochastic term as a sum of the Wiener noise and a jump process. We point to the effects of the jumps on the return time evolution, a central concern of the econophysics literature. The presence of jumps suggests that the process can be described by an infinitely divisible characteristic function belonging to the De Finetti class. We then extend the De Finetti functions to a generalized nonlinear model and show the model to be capable of explaining return behavior.

Validation of reactor noise linear stability methods by means of advanced stochastic differential equation models

The aim of this paper is to show a validation method of a stability monitor using a BWR model with multiple Wiener noise sources, of additive and multiplicative nature. This model is solved using the modern methods to integrate stochastic differential equation systems, that are based on the stochastic Îto-Taylor expansion, and developed by Kloeden and Platen (1995), Kloeden et al. (1994). The synthetic signals generated with this BWR reduced order model with multiple Wiener processes are then used to obtain what are the optimal ways of filtering the signals for the different methods to estimate the decay ration (DR) and the natural frequency ( ω) of the system. Also, for each DR estimation method, we study what is the optimal combination of algorithms to obtain the order and coefficients of the AR model that yields the best prediction of the reactor stability parameters for a broad range of DR values.

Regular dependence on initial data for stochastic evolution equations with multiplicative Poisson noise

We prove existence, uniqueness and Lipschitz dependence on the initial datum for mild solutions of stochastic partial differential equations with Lipschitz coefficients driven by Wiener and Poisson noise. Under additional assumptions, we prove Gâteaux and Fréchet differentiability of solutions with respect to the initial datum. As an application, we obtain gradient estimates for the resolvent associated to the mild solution. Finally, we prove the strong Feller property of the associated semigroup.

Population stochastic modelling (PSM)—An R package for mixed-effects models based on stochastic differential equations

The extension from ordinary to stochastic differential equations (SDEs) in pharmacokinetic and pharmacodynamic (PK/PD) modelling is an emerging field and has been motivated in a number of articles [N.R. Kristensen, H. Madsen, S.H. Ingwersen, Using stochastic differential equations for PK/PD model development, J. Pharmacokinet. Pharmacodyn. 32 (February(1)) (2005) 109–141; C.W. Tornøe, R.V. Overgaard, H. Agersø, H.A. Nielsen, H. Madsen, E.N. Jonsson, Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations, Pharm. Res. 22 (August(8)) (2005) 1247–1258; R.V. Overgaard, N. Jonsson, C.W. Tornøe, H. Madsen, Non-linear mixed-effects models with stochastic differential equations: implementation of an estimation algorithm, J. Pharmacokinet. Pharmacodyn. 32 (February(1)) (2005) 85–107; U. Picchini, S. Ditlevsen, A. De Gaetano, Maximum likelihood estimation of a time-inhomogeneous stochastic differential model of glucose dynamics, Math. Med. Biol. 25 (June(2)) (2008) 141–155]. PK/PD models are traditionally based ordinary differential equations (ODEs) with an observation link that incorporates noise. This state-space formulation only allows for observation noise and not for system noise. Extending to SDEs allows for a Wiener noise component in the system equations. This additional noise component enables handling of autocorrelated residuals originating from natural variation or systematic model error. Autocorrelated residuals are often partly ignored in PK/PD modelling although violating the hypothesis for many standard statistical tests. This article presents a package for the statistical program R that is able to handle SDEs in a mixed-effects setting. The estimation method implemented is the FOCE 1 1 FOCE—First-Order Conditional Estimation. approximation to the population likelihood which is generated from the individual likelihoods that are approximated using the Extended Kalman Filter’s one-step predictions.

On one property of the Wiener integral and its statistical application

For the Wiener integral, one property of inversion is established. This property is used for the construction of nonparametric statistical estimation of the unknown logarithmic derivative for the distribution of random processes, which is observed in Wiener noise.

On Continuous Time Ergodic Filters with Wrong Initial Data

For a class of nonuniformly ergodic Markov diffusions, under observations subject to a Wiener noise, it is shown that a wrong initial data is forgotten with a certain rate in certain topologies.

Signal and noise transfer in spatiotemporal quantum-based imaging systems

Fourier-based transfer theory is extended into the temporal domain to describe both spatial and temporal noise processes in quantum-based medical imaging systems. Lag is represented as a temporal scatter in which the release of image quanta is delayed according to a probability density function. Expressions describing transfer of the spatiotemporal Wiener noise power spectrum through quantum gain and scatter processes are derived. Lag introduces noise correlations in the temporal domain in proportion to the correlated noise component only. The effect of lag is therefore dependent on both spatial and temporal physical processes. A simple model of a fluoroscopic system shows that image noise is reduced by a factor that is similar to Wagner's information bandwidth integral, which depends on the temporal modulation transfer function.

Dynamics of transport under random fluxes on the boundary

The impact of boundary noise on the dynamical evolution of the scalar transport equation in shear flows is studied, taking off from earlier studies in shear-flow dispersion in internal waves, a mechanism for horizontal mixing in the ocean. In particular, we model a gravity current evolving under an assumed shear-flow. The transport equation is deterministic, with a noise term at the inlet boundary. This was motivated by observed seasonal fluctuations in some known sources of salty, dense water in the oceans, like the Red Sea overflow, as well as observed thermal and saline anomalies in the thermohaline circulation. The noises used were: Wiener white, Wiener colored, Lévy white, and Lévy colored noise. Lévy processes form a more general class of processes which are generally non-Gaussian in distribution, and may have infinitely many jumps in finite time. They have been used to model pollutant point-sources, the flight time of particles in vortices, and linear and nonlinear anomalous diffusion. The major finding was that white noises (Wiener and Lévy ) and colored Wiener noise all have the effect of impeding the diffusion process, by as much as 33%. However, colored Lévy noise (non-Gaussian, time-correlated) does not have this effect on diffusion. This would suggest that time-correlation is more important in distinguishing noises than the distribution of the process that produced the noise. This also explains why Lévy colored noise showed great sensitivity to the stability parameter α, while Lévy white noise is unaffected by its stability parameter.