An innovative framework for the control of stochastic process by means of an optimization problem on the Fokker-Planck equation is presented. The time dependent probability density function (PDF) as representative of the dynamical state of a stochastic system is used, hence the governing Kolmogorov-Fokker-Planck-type (KFP) equation is employed as a constraint for the minimization of a cost function. The problem to find a controller that minimizes the cost function can be solved by solving an optimality system of time dependent forward and backward partial differential equations. A short review of control objectives, KFP equations and numerical techniques to tackle the optimization problem, is shown by using models from biology, physics, and finance.

Bio. Mario Annunziato is a researcher in Mathematics, in the field of Numerical analysis at "Università degli Studi di Salerno" since 2004. He is also a member of "Gruppo Nazionale di Calcolo Scientifico, Instituto Nazionale di Alta Mathemaica". He has received his Ph.D. degree in Physics at " Università degli Studi di Pisa" in 2000. He obtained a degree of laurea in Physics at "Rome University - La Sapienza" in 1995.

His research interests focus on numerical solutions of time dependent Partial Differential Equations (PDE) and Integral Equations, related to stochastic processes and stochastic optimal control.