Langevin equation stochastic differential equations

These general considerations suggest a Langevin description (stochastic differential equation) for the time evolution of the bead positions r, ... 

Hamiltonian, but in practice one often begins with a phenomenological set of equations. The set of macrovariables are chosen to include the order parameter and all otlier slow variables to which it couples. Such slow variables are typically obtained from the consideration of the conservation laws and broken synnnetries of the system. The remaining degrees of freedom are assumed to vary on a much faster timescale and enter the phenomenological description as random themial noise. The resulting coupled nonlinear stochastic differential equations for such a chosen relevant set of macrovariables are collectively referred to as the Langevin field theory description. 

The exponential decay predicted by the Onsager-Machlup theory, and by the Langevin and similar stochastic differential equations, is not consistent with the conductivity data in Fig. 8. This and the earlier figures show a constant value for >.(x) at larger times, rather than an exponential decay. It may be that if the data were extended to significantly larger time scales it would exhibit exponential decay of the predicted type. 

One example of a stochastic differential equation is the Langevin equation (IX.1.1). In fact, in most of the mathematical literature the name stochastic differential equation is restricted to this equation, which they call ltd equation. The emphasis on this special case is misdirected. If the equation... 

Setting W(t) = (2kBT )l/2W(t), the Langevin relation—describing the motion of the particle in the phase space—can be written as a two-variable stochastic differential equation system of the form of... 

B. Two-Dimensional Stochastic Differential Equation of the Langevin Type...66... 

In order to understand how the algorithm actually works and to construct an explicit expression for the error it is not convenient to work with the metadynamics equations (12) in their full generality. Instead, we notice that the finite temperature dynamics of the collective variables satisfies, under rather general conditions, a stochastic differential equation [54,55]. Furthermore, in real systems the quantitative behavior of metadynamics is perfectly reproduced by the Langevin equation in its strong friction limit [56]. This is due to the fact that all the relaxation times are usually much smaller than the typical diffusion time in the CV space. Hence, we model the CVs evolution with a Langevin t3rpe dynamics ... 

We underline that the usage of stochastic methods in many particle physics was initiated by Albert Einstein in 1905 working on heavy particles immersed in liquids and which are thus permanently agitated by the molecules of the surrounding liquid. Whereas Einstein formulated an evolution law for the probability P(r, t) to And the particle in a certain position r at time t Paul Langevin formulated a stochastic equation of motion, i.e. a stochastic differential equation for the time dependent position r t) itself. 

In the previous chapter we described numerical methods for solving the equations of Langevin dynamics, a system of stochastic differential equations that sample the canonical ensemble. The methods allow us to compute averages of the form... 

The remainder of this section is devoted to the derivation of Eq.[54]. Besides the mathematics we also define the range of applicability of simulations based on the Nernst-Planck equation. The starting point for deriving the Nernst-Planck equation is Langevin s equation (Eq. [45]). A solution of this stochastic differential equation can be obtained by finding the probability that the solution in phase space is r, v at time t, starting from an initial condition ro, Vo at time = 0. This probability is described by the probability density function p r, v, t). The basic idea is to find the phase-space probability density function that is a solution to the appropriate partial differential equation, rather than to track the individual Brownian trajectories in phase space. This last point is important, because it defines the difference between particle-based and flux-based simulation strategies. 

In Langevin dynamics, a stochastic differential equation is used to simulate movements of molecules. This equation is also known as the Langevin equation ... 

Equation 13.76 is a stochastic differential equation that can describe the time evolution of variable X, which is under the influence of noise. Generally, the Langevin equation can be used to sample the probability distribution P(X, t). Indeed it can be proven that the solutions of the Fokker-Planck equation and of the Langevin equation are equivalent (see MoyaTs text in Further reading). 

Kramers solution of the barrier crossing problem [45] is discussed at length in chapter A3.8 dealing with condensed-phase reaction dynamics. As the starting point to derive its simplest version one may use the Langevin equation, a stochastic differential equation for the time evolution of a slow variable, the reaction coordinate r, subject to a rapidly statistically fluctuating force F caused by microscopic solute-solvent interactions under the influence of an external force field generated by the PES F for the reaction... 

We further discuss how quantities typically measured in the experiment (such as a rate constant) can be computed with the new formalism. The computations are based on stochastic path integral formulation [6]. Two different sources for stochasticity are considered. The first (A) is randomness that is part of the mathematical modeling and is built into the differential equations of motion (e.g. the Langevin equation, or Brownian dynamics). The second (B) is the uncertainty in the approximate numerical solution of the exact equations of motion. 

Constraints may be introduced either into the classical mechanical equations of motion (i.e., Newton s or Hamilton s equations, or the corresponding inertial Langevin equations), which attempt to resolve the ballistic motion observed over short time scales, or into a theory of Brownian motion, which describes only the diffusive motion observed over longer time scales. We focus here on the latter case, in which constraints are introduced directly into the theory of Brownian motion, as described by either a diffusion equation or an inertialess stochastic differential equation. Although the analysis given here is phrased in quite general terms, it is motivated primarily by the use of constrained mechanical models to describe the dynamics of polymers in solution, for which the slowest internal motions are accurately described by a purely diffusive dynamical model. 

The Langevin equation is the prototype of a stochastic differential equation, i.e., a differential equation whose coefficients are random functions of time with given stochastic properties, see chapter XVI. It defines V(t) as a stochastic process when an initial condition is supplied, say F(0) = F0. Then... 

---