Stochastic partial differential equations driven by Levy space-time white noise

In this paper we develop a white noise framework for the study of stochastic partial differential equations driven by a d-parameter (pure jump) Levy white noise. As an example we use this theory to solve the stochastic Poisson equation with respect to Levy white noise for any dimension d. The solution is a stochastic distribution process given explicitly. We also show that if d\leq 3, then this solution can be represented as a classical random field in L2(\mu ), where \mu is the probability law of the Levy process. The starting point of our theory is a chaos expansion in terms of generalized Charlier polynomials. Based on this expansion we define Kondratiev spaces and the Levy Hermite transform

A stochastic maximum principle via Malliavin calculus

This paper considers a controlled It\^o-L\'evy process where the information available to the controller is possibly less than the overall information. All the system coefficients and the objective performance functional are allowed to be random, possibly non-Markovian. Malliavin calculus is employed to derive a maximum principle for the optimal control of such a system where the adjoint process is explicitly expressed

Symmetries of the ratchet current

Recent advances in nonequilibrium statistical mechanics shed new light on the ratchet effect. The ratchet motion can thus be understood in terms of symmetry (breaking) considerations. We introduce an additional symmetry operation besides time-reversal, that effectively reverses the nonequilibrium driving. That operation of field-reversal combined with time-reversal decomposes the nonequilibrium action so to clarify under what circumstances the ratchet current is a second order effect around equilibrium, what is the direction of the ratchet current and what are possibly the symmetries in its fluctuations.Comment: 13 pages, heavily extended versio

Fokker-Planck PIDE for McKean-Vlasov jump diffusions and applications to HJB equations and optimal control

The purpose of this paper is to study optimal control of McKean-Vlasov (mean-field) stochastic differential equations with jumps (McKean-Vlasov jump diffusions, for short). To this end, we first prove a Fokker-Planck equation for the law of the solution of such equations. Then we study the situation when the law is absolutely continuous with respect to Lebesgue measure. In that case the Fokker-Planck equation reduces to a deterministic integro-differential equation (PIDE) for the Radon-Nikodym derivative of the law. Combining this equation with the original state equation, we obtain a Markovian system for the state and its law. Furthermore, we apply this to formulate an Hamilton-Jacobi-Bellman (HJB) equation for the optimal control of McKean-Vlasov stochastic differential equations with jumps. Finally we apply these results to solve explicitly the following problems: Linear-quadratic optimal control of stochastic McKean-Vlasov jump diffusions. Optimal consumption from a cash flow modelled as a stochastic McKean-Vlasov differential equation with jumps.Comment: 2

Optimal Consumption and Portfolio in a Jump Diffusion Market with Proportional Transaction Costs

We study the optimal consumption and portfolio in a jump diffusion market with proportional transaction costs. We show that the solution in the jump diffusion case has the same form as in the pure diffusion case; in particular, (under some assumptions) there is a transaction cone D such that it is optimal to make no transactions as long as the wealth position remains in D and to sell/buy stocks according to local time on the boundary of D. The associated integro-differential variational inequality is studied by using the theory of viscosity solutions

Langevin Equation for the Density of a System of Interacting Langevin Processes

We present a simple derivation of the stochastic equation obeyed by the density function for a system of Langevin processes interacting via a pairwise potential. The resulting equation is considerably different from the phenomenological equations usually used to describe the dynamics of non conserved (Model A) and conserved (Model B) particle systems. The major feature is that the spatial white noise for this system appears not additively but multiplicatively. This simply expresses the fact that the density cannot fluctuate in regions devoid of particles. The steady state for the density function may however still be recovered formally as a functional integral over the coursed grained free energy of the system as in Models A and B.Comment: 6 pages, latex, no figure

Distribution of the Oscillation Period in the Underdamped One Dimensional Sinai Model

We consider the Newtonian dynamics of a massive particle in a one dimemsional random potential which is a Brownian motion in space. This is the zero temperature nondamped Sinai model. As there is no dissipation the particle oscillates between two turning points where its kinetic energy becomes zero. The period of oscillation is a random variable fluctuating from sample to sample of the random potential. We compute the probability distribution of this period exactly and show that it has a power law tail for large period, P(T)\sim T^{-5/3} and an essential singluarity P(T)\sim \exp(-1/T) as T\to 0. Our exact results are confirmed by numerical simulations and also via a simple scaling argument.Comment: 9 pages LateX, 2 .eps figure

Control of Multi-level Voltage States in a Hysteretic SQUID Ring-Resonator System

In this paper we study numerical solutions to the quasi-classical equations of motion for a SQUID ring-radio frequency (rf) resonator system in the regime where the ring is highly hysteretic. In line with experiment, we show that for a suitable choice of of ring circuit parameters the solutions to these equations of motion comprise sets of levels in the rf voltage-current dynamics of the coupled system. We further demonstrate that transitions, both up and down, between these levels can be controlled by voltage pulses applied to the system, thus opening up the possibility of high order (e.g. 10 state), multi-level logic and memory.Comment: 8 pages, 9 figure