Analytical solution of stochastic differential equation by multilayer perceptron neural network approximation of Fokker–Planck equation

The Fokker–Planck equation is a useful tool to analyze the transient probability density function of the states of a stochastic differential equation. In this paper, a multilayer perceptron neural network is utilized to approximate the solution of the Fokker–Planck equation. To use unconstrained optimization in neural network training, a special form of the trial solution is considered to satisfy the initial and boundary conditions. The weights of the neural network are calculated by Levenberg–Marquardt training algorithm with Bayesian regularization. Three practical examples demonstrate the efficiency of the proposed method.     

The density evolution of the killed McKean–Vlasov process

ABSTRACT

The density evolution of McKean–Vlasov stochastic differential equations in the presence of an absorbing boundary is analysed where the solution to such equations corresponds to the dynamics of partially killed large populations. By using a fixed point theorem, we show that the density evolution is characterized as the solution of an integro-differential Fokker–Planck equation with Cauchy–Dirichlet data. This problem arises naturally within mean field game theory.     

Regret bounds for Narendra-Shapiro bandit algorithms

Narendra-Shapiro (NS) algorithms are bandit-type algorithms developed in the 1960s. NS-algorithms have been deeply studied in infinite horizon but little non-asymptotic results exist for this type of bandit algorithms. In this paper, we focus on a non-asymptotic study of the regret and address the following question: are Narendra-Shapiro bandit algorithms competitive from this point of view? In our main result, we obtain some uniform explicit bounds for the regret of (over)-penalized-NS algorithms. We also extend to the multi-armed case some convergence properties of penalized-NS algorithms towards a stationary Piecewise Deterministic Markov Process (PDMP). Finally, we establish some new sharp mixing bounds for these processes.     

Modelling Fruit Characteristics During Apple Maturation: A Stochastic Approach

At present, mathematical models to predict the change of fruit quality attributes during apple maturation are deterministic and do not take into account the large natural variability of fruit quality attributes during the growing season. In this work a stochastic system approach was developed to describe the quality evolution of fruit. The basic dynamics of fruit quality evolution was represented by means of a stochastic system, in which the initial conditions and the model parameters were specified as random variables together with their probability density functions. A fundamental approach from stochastic systems theory was used to compute the propagation of the probability density functions of fruit quality attributes, which requires the numerical solution of the Fokker–Planck equation.     

The Vlasov–Poisson–Fokker–Planck equation in an interval with kinetic absorbing boundary conditions

We study the initial–boundary value problem for the Vlasov–Poisson–Fokker–Planck equations in an interval with absorbing boundary conditions. We first prove the existence of weak solutions of the linearized equation in an interval with absorbing boundary conditions. Moreover, the weak solution converges to zero exponentially in time. Then we extend the above results to the fully nonlinear Vlasov–Poisson–Fokker–Planck equations in an interval with absorbing boundary conditions; the existence and the longtime behavior of weak solutions. Finally, we prove that the weak solution is actually a classical solution by showing the hypoellipticity of the solution away from the grazing set and the Hölder continuity of the solution up to the grazing set.     

Smooth solution of a nonlocal Fokker–Planck equation associated with stochastic systems with Lévy noise

It is shown that the solution of a nonlocal Fokker–Planck equation is smooth with respect to both time and space variable whenever the divergence of the smooth drift has a lower bound.     

Necessary and sufficient optimality conditions for control of piecewise deterministic markov processes

Piecewise deterministic Markov processes (PDPs) are continuous time homogeneous Markov processes whose trajectories are solutions of ordinary differential equations with random jumps between the different integral curves. Both continuous deterministic motion and the random jumps of the processes are controlled in order to minimize the expected value of a performance criterion involving discounted running and boundary costs. Under fairly general assumptions, we will show that there exists an optimal control, that the value function is Lipschitz continuous and that a generalized Bellman-Hamilton-Jacobi (BHJ) equation involving the Clarke generalized gradient is a necessary and sufficient optimality condition for the problem.     

Numerical solution for a class of SPDEs over bounded domains

Parabolic stochastic partial differential Equations (SPDEs) with multiplicative noise play a central rôle in nonlinear filtering. More precisely, the conditional distribution of a partially observed diffusion solves the normalized version of an equation of this type. We show that one can approximate the solution of the SPDE by the (unweighted) empirical measure of a finite system of interacting particle for the case when the diffusion evolves in a compact state space with reflecting boundary. This approximation differs from existing approximations where the particles are weighted and the particle interaction arises through the choice of the weights and not at the level of the particles' motion as it is the case in this work. The system of stochastic differential equations modelling the trajectories of the particles is approximated by the recursive projection scheme introduced by Pettersson [Stoch. Process. Appl. 59(2) (1995), pp. 295–308].     

Decay and stability of solutions to the Fokker–Planck–Boltzmann equation in

In this paper, we use the method of constructing the compensating function introduced by Kawashima and the standard energy method to study the global existence of solutions to the Fokker–Planck–Boltzmann equation in the whole space. The time decay and uniform stability of solutions to the global Maxwellian are also obtained.     

Dynamics of tumor virotherapy: A deterministic and stochastic model approach

Cancer virotherapy is studied in mathematical modeling to improve tumor elimination. Since various oncolytic viruses are used for cancer therapy and virus selection is an important research problem, we, therefore, constructed deterministic and stochastic models of cancer-virus dynamics. We investigated virus characteristic parameter sensitivities using a reproduction ratio. Locally and globally asymptotically stable equilibrium points that are respectively related to therapy failure/partial success and therapy failure were determined. A stochastic system was derived from the deterministic model. Tumor extinction probabilities depending on changing parameter values were investigated. Results suggest that viruses with high infection rates and optimal cytotoxicity are effective for cancer treatment.     

[3, 3] Padé approximation method for solving space fractional Fokker–Planck equations

The fractional Fokker–Planck equation has been used in various areas of engineering and physics. In this paper, we proposed a novel numerical scheme for solving the space fractional Fokker–Planck equation with the help of the [3, 3] Padé approximation. It is proved that the numerical method is unconditionally stable in view of the matrix analysis method. Finally, a numerical example is proposed to prove the effectiveness of the numerical scheme.