First-passage problems for degenerate two-dimensional diffusion processes

Optimal control problems for degenerate two-dimensional diffusion processes are considered. The processes could serve as models for wear processes. The objective is to make the controlled process leave the continuation region through a given part of the boundary. Explicit and exact solutions are obtained for important processes such as the geometric Brownian motion and the Ornstein-Uhlenbeck process.

In this chapter we express the classical Wigner function by a degenerate diffusion process [164] solving a randomly perturbed Newton equation. We discuss first some estimates on the solution of these equations and the long time behaviour of the diffusion process. We discuss a relation between the classical Wigner function and the quantum one. We approximate the large time behaviour of the density matrix by the classical Wigner function. In this way we obtain some estimates on the decoherence in space expressed by the correlation functions of the diffusion process.

Four methods for the simulation of the Wiener process with constant drift and variance are described. These four methods are (1) approximating the diffusion process by a random walk with very small time steps; (2) drawing directly from the joint density of responses and reaction time by means of a (possibly) repeated application of a rejection algorithm; (3) using a discrete approximation to the stochastic differential equation describing the diffusion process; and (4) a probability integral transform method approximating the inverse of the cumulative distribution function of the diffusion process. The four methods for simulating response probabilities and response times are compared on two criteria: simulation speed and accuracy of the simulation. It is concluded that the rejection-based and probability integral transform method perform best on both criteria, and that the stochastic differential approximation is worst. An important drawback of the rejection method is that it is applicable only to the Wiener process, whereas the probability integral transform method is more general.

Necessary and sufficient conditions are given for transforming (constructively) a one-dimensional diffusion process described by a Kolmogorov equation into the Wiener process. These conditions are shown to be equivalent to invariance of a parabolic partial differential equation under a six-parameter Lie group of point transformations. Moreover these conditions correspond to a significant generalization of Cherkasov’s result; i.e., a much wider class of Kolmogorov equations can be derived from the Wiener process than was previously realized.

By a well-known theorem of P. Levy, if $(X_t)$ is a standard Brownian motion on $\mathbb{R}$ with $X_0 = 0$ and if $H_t = \min_{u \leq t}X_u$, then $(Y_t) = (X_t - H_t)$ is Brownian motion with 0 as a reflecting lower boundary. More generally, if $X$ is allowed to have nonzero drift or a reflecting lower boundary at $A < 0$, then the process $Y = X - H$ is still a diffusion process. We prove the converse result: If $X$ is a diffusion on an interval $I \subset \mathbb{R}$ which contains 0 as an interior point, and if $(Y_t) = (X_t - H_t)$ is a time homogeneous strong Markov process (when $X_0 = 0$), then $X$ must be a Brownian motion on $I$ (with drift $\mu$, variance parameter $\sigma^2 > 0$, killing rate $c \geq 0$ and reflection at $\inf I$ in case $\inf I > -\infty$).

This study formulates an open problem on spatial localization for quasilinear parabolic equations of pLaplacian type on the half-space R+N, with smooth, nonnegative initial data and boundary conditions that grow unbounded over time. While localization has been widely studied under homogeneous or decaying boundary conditions, its persistence under unbounded boundary input remains unresolved. We introduce two forms of spatial localization: effective localization, where the solution remains within a finite spatial domain, and strict localization, where it vanishes beyond a fixed boundary. The problem examines how the structure of the nonlinearities A(v) and B(v), and the growth rate of the boundary function ϕ influence solution confinement or spread. The study extends classical localization theory to degenerate and singular diffusion processes with diffusion exponent p &gt; 1, offering new insight into spatial confinement under persistent external forcing.

The application and universality of a representation of generalized composite signals have been substantiated. The problem of synthesizing them has been stated, and its geometric treatment has been given; the generality of such a statement of the problem has been demonstrated.

Let Y(t) be a one-dimensional jump-diffusion process and X(t) be defined by dX(t)=ρ[X(t),Y(t)]dt, where ρ(·,·) is either a strictly positive or negative function. First-passage-time problems for the degenerate two-dimensional process (X(t),Y(t)) are considered in the case when the process leaves the continuation region at the latest at the moment of the first jump, and the problem of optimally controlling the process is treated as well. A particular problem, in which ρ[X(t),Y(t)]=Y(t)−X(t) and Y(t) is a standard Brownian motion with jumps, is solved explicitly.

We study the sensitivity of the densities of non degenerate diffusion processes and related Markov Chains with respect to a perturbation of the coefficients. Natural applications of these results appear in models with misspecified coefficients or for the investigation of the weak error of the Euler scheme with irregular coefficients.

Considering the dynamic diffusion in degradation process, we propose an integrated Wiener process with a nonlinear drift and a stochastic volatility for degradation modelling. In our model, the drift and stochastic volatility are estimated nonparametrically to avoid the model misspecification issue. The key innovation of this study is the estimation of stochastic volatility which adopts the functional principal component analysis method and applies it to bipower variance. This estimation approach is able to effectively capture both cross-unit variations and time-varying features within the diffusion process. Also, the proposed model can reduce the estimation bias caused by jump points, which are common phenomenon in degradation process. Furthermore, the distribution of remaining useful life and the mean time to failure are both obtained in explicit forms. Finally, the proposed model and the nonparametric estimation method are illustrated by two simulation experiments and two real cases.

Approximation of slow moving interface phase change problems using a generalized Fourier series and the CVBEM

The direct Lyapunov method for stability of dynamical systems is extended to the almost sure stability of Ito stochastic differential equations by means of semicontinuous Lyapunov functions satisfying a suitable system of partial differential inequalities in viscosity sense. The a.s. stabilizability of controlled degenerate diffusion processes is also treated. The key tools are some geometric characterizations of the invariance and viability properties of closed sets for controlled diffusions obtained by PDE-viscosity methods.

A CLT for degenerate diffusions with periodic coefficients, and application to homogenization of linear PDEs

We study the sequential hypothesis testing and quickest change-point (or disorder) detection problems with linear delay penalty costs for observable Wiener processes under (constantly) delayed detection times. The method of proof consists of the reduction of the associated delayed optimal stopping problems for one-dimensional diffusion processes to the equivalent free-boundary problems and solution of the latter problems by means of the smooth-fit conditions. We derive closed-form expressions for the Bayesian risk functions and optimal stopping boundaries for the associated weighted likelihood ratio processes in the original problems of sequential analysis.

Covariance and independence, rotational symmetry, isonormal Gaussian process, independent increments, Brownian motion and bridge, scaling and inversion, Gaussian Markov processes, quadratic variation, path irregularity, strong Markov and reflection properties, Bessel processes, maximum process, arcsine and uniform laws, laws of the iterated logarithm, Wiener integral, spectral and moving-average representations, Ornstein–Uhlenbeck process, multiple Wiener–Ito integrals, chaos expansion of variables and processes.