Stochastic trajectory techniques

Various deterministic and stochastic sampling techniques for path ensembles have been proposed [4-6]. Here we consider only Monte Carlo methods. It is important, however, to be aware that while the path ensemble is sampled with a Monte Carlo procedure each single pathway is a fully dynamical trajectory such as one generated by molecular dynamics. 

The state estimation technique can also be incorporated into the design of optimal batch polymerization control system. For example, a batch reaction time is divided into several control intervals, and the optimal control trajectory is updated online using the molecular weight estimates generated by a model/state state estimator. Of course, if batch reaction time is short, such feedback control of polymer properties would be practically difficult to implement. Nevertheless, the online stochastic estimation techniques and the model predictive control techniques offer promising new directions for the improved control of batch polymerization reactors. 

Particle trajectories can be calculated by utilizing the modern CFD (computational fluid dynamics) methods. In these calculations, the flow field is determined with numerical means, and particle motion is modeled by combining a deterministic component with a stochastic component caused by the air turbulence. This technique is probably an effective means for solving particle collection in complicated cleaning systems. Computers and computational techniques are being developed at a fast pace, and one can expect that practical computer programs for solving particle collection in electrostatic precipitators will become available in the future. 

The general principle of BD is based on Brownian motion, which is the random movement of solute molecules in dilute solution that result from repeated collisions of the solute with solvent molecules. In BD, solute molecules diffuse under the influence of systematic intermolecular and intramolecular forces, which are subject to frictional damping by the solvent, and the stochastic effects of the solvent, which is modeled as a continuum. The BD technique allows the generation of trajectories on much longer temporal and spatial scales than is feasible with molecular dynamics simulations, which are currently limited to a time of about 10 ns for medium-sized proteins. 

The basic idea behind an atomistic-level simulation is quite simple. Given an accurate description of the energetic interactions between a collection of atoms and a set of initial atomic coordinates (and in some cases, velocities), the positions (velocities) of these atoms are advanced subject to a set of thermodynamic constraints. If the positions are advanced stochastically, we call the simulation method Monte Carlo or MG [10]. No velocities are required for this technique. If the positions and velocities are advanced deterministically, we call the method molecular dynamics or MD [10]. Other methods exist which are part stochastic and part deterministic, but we need not concern ourselves with these details here. The important point is that statistical mechanics teUs us that the collection of atomic positions that are obtained from such a simulation, subject to certain conditions, is enough to enable aU of the thermophysical properties of the system to be determined. If the velocities are also available (as in an MD simulation), then time-dependent properties may also be computed. If done properly, the numerical method that generates the trajectories... 

The transition path sampling techniques we have described assume that an initial reactive pathway is available. Generating such a pathway is therefore an important step in applying the method. In the simplest cases, a trajectory connecting A and B can be obtained by running a long molecular dynamics (or stochastic dynamics) simulation. For most applications, however, the... 

Our definition of a committor in Eq. (1.107) is applicable to both stochastic and deterministic dynamics. In the case of deterministic dynamics, care must be taken that fleeting trajectories are initiated with momenta drawn from the appropriate distribution. As discussed in Section III.A.2, global constraints on the system may complicate this distribution considerably. The techniques described in Section III.A.2 and in the Appendix of [10] for shooting moves may be simply generalized to draw initial momenta at random from the proper equilibrium distribution. 

Instead of solving the evolution equation in terms of the orientation tensor, one can simulate the stochastic equation such as Eq. 5.7 for the orientation vector p without the need of closure approximations, using the numerical technique for the simulation of stochastic processes (Ottinger 1996) known as the Brownian dynamics simulation. Once trajectories for aU fibers are obtained, the orientation tensor can be calculated in terms of the ensemble average of the discrete form ... 

The integration of the single-particle joint-PDF transport equation (12.4.1-11) is tedious. Computer requirements for standard CFD techniques rise exponentially with the dimensionality of the joint-PDF. Therefore, micro-PDF methods commonly use a Monte-Carlo approach [Spielman and Levenspiel, 1965 Kattan and Adler, 1967, 1972 Pope, 1981]. A deterministic system is constructed with stochastic particles whose joint-PDF evolves in the same way as the joint-PDF of fluid particles. The trajectories of the so-called conditional particles define a formal solution of the joint-PDF transport equation (12.4.1-11). Ramkrishna [2000] presents details on the computational methods. 

Nowadays, computer simulations are treated as the third fundamental discipline of interface research in addition to the two classieal ones, namely theory and experiment. Based direetly on a microscopie model of the system, eomputer simulations can, in principle at least, provide an exact solution of any physicochemical problem. By far the most common methods of studying adsorption systems by simulations are the Monte Carlo (MC) technique and the molecular dynamics (MD) method. In this ehapter, a description of simidation methods will be omitted because several textbooks and review artieles on the subject are available [274-277]. The present discussion will be restricted to elementary aspects of simulation methods. In the deterministic MD method, the moleeular trajectories are eomputed by solving Newton s equations, and a time-correlated sequenee of configurations is generated. The main advantage of this technique is that it permits the study of time-dependent processes. In MC simulation, a stochastic element is an essential part of the method the trajectories are generated by random walk in configuration space. Struetural and thermodynamic properties are accessible by both methods. 

The resynthesis process results from two simultaneous synthesis processes one for sinusoidal components and the other for the noisy components of the sound (Figure 3.15). The sinusoidal components are produced by generating sinewaves dictated by the amplitude and frequency trajectories of the harmonic analysis, as with additive resynthesis. Similarly, the stochastic components are produced by filtering a white noise signal, according to the envelope produced by the formant analysis, as with subtractive synthesis. Some implementations, such as the SMS system discussed below, generate artificial magnitude and phase information in order to use the Fourier analysis reversion technique to resynthesise the stochastic part. 

Note that most of these algorithms are used to generate a single trajectory, similar to the accelerated molecular dynamics techniques. However, the stochastic MC component does not allow to assign a timescale to the simulation, except in the case of so-called time stamped force bias Monte Carlo (tfMC, see below) [47]. Thus, a comparison in terms of a boost factor with the accelerated dynamics techniques cannot be made. 

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