Random Ordinary Differential Equations Research Articles

Random Fixed Point Theorems and Applications to Random First-Order Vector-Valued Differential Equations

In this paper, we establish several random fixed point theorems for random operators satisfying some iterative condition w.r.t. a measure of noncompactness. We also discuss the case of monotone random operators in ordered Banach spaces. Our results extend several earlier works, including Itoh’s random fixed point theorem. As an application, we discuss the existence of random solutions to a class of random first-order vector-valued ordinary differential equations with lack of compactness.

Uncertainty Quantification of Bifurcations in Random Ordinary Differential Equations

We are concerned with random ordinary differential equations (RODEs). Our main question of interest is how uncertainties in system parameters propagate through the possibly highly nonlinear dynamic...

Numerical solution of stiff random ordinary differential equations via averaged schemes

Random ordinary differential equations (RODEs) are important differential equations that contain a stochastic process in their vector field functions. Examples of such stochastic processes are Brownian motion (i.e., Wiener process) and fractional Brownian motion. Taking a path from the stochastic process, these equations can be considered as ordinary differential equations, but numerical schemes for solving ordinary differential equations don't necessarily preserve their convergence order for these equations. In this paper, an implicit ϑ‐averaged scheme for numerical solution of RODEs is introduced. The proposed method involves averaging the noise inside the vector field and leads to integrals of the noise over discretization subintervals. Pathwise convergence and B‐stability of the above scheme are proved. Eventually, the desired averaged scheme is implemented on a medical model.

Toward a Wong–Zakai Approximation for Big Order Generators

We give a new approximation with respect of the traditional parametrix method of the solution of a parabolic equation whose generator is of big order and under the Hoermander form. This generalizes to a higher order generator the traditional approximation of Stratonovitch diffusion which put in relation random ordinary differential equation (the leading process is random and of finite energy. When a trajectory of it is chosen, the solution of the equation is defined) and stochastic differential equation (the leading process is random and only continuous and we cannot choose a path in the solution which is only almost surely defined). We consider simple operators where the computations can be fully performed. This approximation fits with the semi-group only and not for the full path measure in the case of a stochastic differential equation.

Safety Verification for Random Ordinary Differential Equations

Random ordinary differential equations (RODEs) are ordinary differential equations (ODEs) that contain a stochastic process in their vector field functions. They have been used for many years in a wide range of applications, but have been a shadow existence to stochastic differential equations (SDEs) despite being able to model a wider and often physically more adequate range of disturbances. In this article, we study the safety verification problem over both finite time horizons and the infinite time horizon for RODEs incorporating Wiener processes. Concretely, we investigate the p-safety problem, where we identify the set of initial states from which the probability to satisfy safety specifications is at least p. Based on identifying a set of sample paths whose probability measure is larger than p, we propose a method of reducing stochastic reachability to adversary reachability of ODEs for solving the p-safety problem over finite time horizons. This method permits an efficient lifting of reach-set computation methods for perturbed ODEs to RODEs. In this method, the p-safety problem over finite time horizons is reduced to the problem of inner-approximating robust backward reachable sets for ODEs with time-varying perturbation inputs. We then extend the method to the p-safety problem over the infinite time horizon. Finally, we demonstrate our method on several examples.

Mean-square convergence of numerical methods for random ordinary differential equations

The aim of this work is to analyze the mean-square convergence rates of numerical schemes for random ordinary differential equations (RODEs). First, a relation between the global and local mean-square convergence order of one-step explicit approximations is established. Then, the global mean-square convergence rates are investigated for RODE-Taylor schemes for general RODEs, Affine-RODE-Taylor schemes for RODEs with affine noise, and Ito-Taylor schemes for RODEs with Ito noise, respectively. The theoretical convergence results are demonstrated through numerical experiments.

Selection of a stochastic Landau-Lifshitz equation and the stochastic persistence problem

In this article, we study the persistence of properties of a given classical deterministic differential equation under a stochastic perturbation of two distinct forms: external and internal. The first case corresponds to add a noise term to a given equation using the framework of Itô or Stratonovich stochastic differential equations. The second case corresponds to consider parameter dependent differential equations and to add a stochastic dynamics on the parameters using the framework of random ordinary differential equations. Our main concerns for the preservation of properties are stability/instability of equilibrium points and symplectic/Poisson Hamiltonian structures. We formulate persistence theorem in these two cases and prove that the cases of external and internal stochastic perturbations are drastically different. We then apply our results to develop a stochastic version of the Landau-Lifshitz equation. We discuss, in particular, previous results obtained by Étoré et al. [J. Differ. Equations 257, 2115–2135 (2014)] and we finally propose a new family of stochastic Landau-Lifshitz equations.

Smooth Random Functions, Random ODEs, and Gaussian Processes

The usual way in which mathematicians work with randomness is by a rigorous formulation of the idea of Brownian motion, which is the limit of a random walk as the step length goes to zero. A Brownian path is continuous but nowhere differentiable, and this nonsmoothness is associated with technical complications that can be daunting. However, there is another approach to random processes that is more elementary, involving smooth random functions defined by finite Fourier series with random coefficients or, equivalently, by trigonometric polynomial interpolation through random data values. We show here how smooth random functions can provide a very practical way to explore random effects. For example, one can solve smooth random ordinary differential equations using standard mathematical definitions and numerical algorithms, rather than having to develop new definitions and algorithms of stochastic differential equations. In the limit as the number of Fourier coefficients defining a smooth random function goes to $\infty$, one obtains the usual stochastic objects in what is known as their Stratonovich interpretation.

Numerical schemes for ordinary delay differential equations with random noise

Random ordinary differential equations (RODEs) are ordinary differential equations (ODEs) which have a stochastic process in their vector field functions. They have been used in a wide range of applications such as biology, medicine and engineering and play an important role in the theory of random dynamical systems. RODEs can be investigated pathwise as deterministic ODEs, however, the classical numerical methods for ODEs do not attain original order of convergence because the stochastic process has at most Hölder continuous sample paths and the resulting vector is also at most Hölder continuous in time. Recently, Jenzen & Kloeden derived new class of numerical methods for RODEs using integral versions of implicit Taylor-like expansions and developed arbitrary higher order schemes for RODEs. Their idea can be applied to random ordinary delay differential equations (RODDEs) by implementing Taylor-like expansions in the corresponding delay term. In this paper, numerical methods for RODDEs are systematically constructed based on Taylor-like expansions and they are applied to virus dynamics model with random fluctuations and time delay.

Long term behavior of a random Hopfield neural lattice model

A Hopfield neural lattice model is developed as the infinite dimensional extension of the classical finite dimensional Hopfield model. In addition, random external inputs are considered to incorporate environmental noise. The resulting random lattice dynamical system is first formulated as a random ordinary differential equation on the space of square summable bi-infinite sequences. Then the existence and uniqueness of solutions, as well as long term dynamics of solutions are investigated.

Rough flows and homogenization in stochastic turbulence

We provide in this work a tool-kit for the study of homogenisation of random ordinary differential equations, under the form of a friendly-user black box based on the technology of rough flows. We illustrate the use of this setting on the example of stochastic turbulence.

The Variational Methods for Solving Random Models

This paper studies the solutions of variational methods for random ordinary (partial) differential equations in L2−space. These methods are called Galerkin method, Petrov-Galerkin method, Least-Squares method and Collocation method. Some basic properties of these methods where applying on random problems will be shown throughout some numerical examples.

The Variational Methods for Solving Random Models

This paper studies the solutions of variational methods for random ordinary (partial) differential equations in L2−space. These methods are called Galerkin method, Petrov-Galerkin method, Least-Squares method and Collocation method. Some basic properties of these methods where applying on random problems will be shown throughout some numerical examples.

Solving Random Ordinary Differential Equations on GPU Clusters using Multiple Levels of Parallelism

Random ordinary differential equations (RODEs) perfectly describe classes of time-dependent problems with stochastic disturbances that are of utmost importance in science and engineering. Both their pathwise solution concept leading to a massive amount of simulations and the form of the numerical solvers for RODEs contain high potential for efficient parallelization approaches. We analyze for the first time a high performance computing parallelization relying on GPU clusters to exploit the underlying three levels of parallelism for the example of the Kanai--Tajimi earthquake model in its RODE form. We identify four basic building blocks of the application which are valid also for general RODE scenarios. We optimized and benchmarked the implementation of the four building blocks separately to be able to select the best individual parameter settings. This allows for a comparison of the total performance of the overall application which shows excellent scaling results also on large GPU clusters. The results can be generalized to both other RODE applications and other approaches relying on a parallel, efficient generation of pseudorandom numbers or realization of the Ornstein--Uhlenbeck process.

Limits of random differential equations on manifolds

Consider a family of random ordinary differential equations on a manifold driven by vector fields of the form $\sum_kY_k\alpha_k(z_t^\epsilon(\omega))$ where $Y_k$ are vector fields, $\epsilon$ is a positive number, $z_t^\epsilon$ is a ${1\over \epsilon} {\mathcal L}_0$ diffusion process taking values in possibly a different manifold, $\alpha_k$ are annihilators of $ker ({\mathcal L}_0^*)$. Under H\"ormander type conditions on ${\mathcal L}_0$ we prove that, as $\epsilon $ approaches zero, the stochastic processes $y_{t\over \epsilon}^\epsilon$ converge weakly and in the Wasserstein topologies. We describe this limit and give an upper bound for the rate of the convergence.

Multi-step methods for random ODEs driven by Itô diffusions

Linear multi-step methods are derived for random ordinary differential equations (RODEs) driven by the solutions of Itô stochastic differential equations (SODEs) via strong Itô–Taylor schemes for SODEs. Due to the special structure of the RODE–SODE pair it is not necessary to restrict the intensity of the noise. Pathwise convergence is established as well as the B-stability of implicit multi-step methods. Numerical comparisons are provided for explicit schemes applied to a low dimensional RODE and implicit schemes applied to a high dimensional RODE obtained with the method of lines by spatially discretizing a random partial differential equation with finite difference quotients.

Numerical schemes for random ODEs with affine noise

Numerical schemes for random ordinary differential equations, abbreviated RODEs, with an affine structure can be derived in a similar way as for affine control systems using Taylor expansions that resemble stochastic Taylor expansions for Stratonovich stochastic differential equations. The driving noise processes can be quite general, such as Wiener processes or fractional Brownian motions with continuous sample paths or compound Poisson processes with piecewise constant sample paths, and even more general noises. Such affine-Taylor schemes of arbitrarily high order are constructed here. It is shown how their structure simplifies when the noise terms are additive or commutative. A derivative free counterpart is given and multi-step schemes are derived too. Numerical comparisons are provided for various explicit one-step and multi-step schemes in the context of a toggle switch model from systems biology.

A random Laplace transform method for solving random mixed parabolic differential problems

This paper deals with the explicit solution of random mixed parabolic equations in unbounded domains by using the random Laplace transform to second order stochastic processes. The mean square random Laplace operational calculus is stated and its application to the random parabolic equation together with previous results of the underlying random ordinary differential equations allow us to obtain an explicit solution of the problem. A numerical example, which includes simulations, illustrates the developed method.

Extreme Analysis of a Random Ordinary Differential Equation

In this paper we consider a one dimensional stochastic system described by an elliptic equation. A spatially varying random coefficient is introduced to account for uncertainty or imprecise measurements. We model the logarithm of this coefficient by a Gaussian process and provide asymptotic approximations of the tail probabilities of the derivative of the solution.

Extreme Analysis of a Random Ordinary Differential Equation

In this paper we consider a one dimensional stochastic system described by an elliptic equation. A spatially varying random coefficient is introduced to account for uncertainty or imprecise measurements. We model the logarithm of this coefficient by a Gaussian process and provide asymptotic approximations of the tail probabilities of the derivative of the solution.