Phase-space information

The numbers

investigated statistically. For instance, one may calculate the probabihty distribution Pn function value (p if one randomly picks a mesh point j,k). Or one may look at correlations between

projected directly from quantum wave functions. Thus, classical and quantum dynamics can be compared on an equal footing. All these questions are still under active investigation. 

Here, is arbitrary time in t in the vicinity of saddles. These expressions imply that, even though almost all degrees of freedom of the system are chaotic, the final state (and initial state) may have been determined a priori. For example, if the trajectories that have initiated from S( j = 0) at time to have a > 0, the final state has already been determined at the time Iq when the system has just left the S qi = 0) to be a stable state directed by > 0. Similarly, from only the phase-space information at t = to (the sign of j ), one can grasp whether the system on S q j = 0) at time to has climbed from either stable state, that is, reactant or product, without calculating any time-reversed trajectory [62]. 

Since the phase space of a dissipative dynamical system contracts with time, we know that, in the long time limit, t oo, the motion will be confined to some fixed attractor, A. Moreover, becaust of the contraction, the dimension, D, of A, must be lower than that of the actual phase space. While D adds little information in the case of a noiichaotic attractor (we know immediately, and trivially, for example, that all fixed-points have D = 0, limit cycles have D = 1, 2-tori have D = 2, etc.), it is of significant interest for strange attractors, whose dimension is typically non-integer valued. Three of the most common measures of D are the fractal dimension, information dimension and correlation dimension. 

The moving invariant manifolds determine the reactivity or nonreactivity of an individual trajectory under the influence of a specific noise sequence. They thus provide the most detailed microscopic information on the reaction dynamics that one can possibly possess. In practice, though, one is more often interested in macroscopic quantities that are obtained by averaging over the noise. To illustrate that such quantities can easily be derived from the microscopic information encoded in the TS trajectory, we calculate the probability for a trajectory starting at a point (q, v) in the space-fixed phase space to end up on the product side of the... 

Order and polydispersity are key parameters that characterize many self-assembled systems. However, accurate measurement of particle sizes in concentrated solution-phase systems, and determination of crystallinity for thin-film systems, remain problematic. While inverse methods such as scattering and diffraction provide measures of these properties, often the physical information derived from such data is ambiguous and model dependent. Hence development of improved theory and data analysis methods for extracting real-space information from inverse methods is a priority. 

It is clear that the strong form of the QCT is impossible to obtain from either the isolated or open evolution equations for the density matrix or Wigner function. For a generic dynamical system, a localized initial distribution tends to distribute itself over phase space and either continue to evolve in complicated ways (isolated system) or asymptote to an equilibrium state (open system) - whether classically or quantum mechanically. In the case of conditioned evolution, however, the distribution can be localized due to the information gained from the measurement. In order to quantify how this happ ens, let us first apply a cumulant expansion to the (fine-grained) conditioned classical evolution (5), resulting in the equations for the centroids (x = (t), P= (P ,... 

The one-point joint composition PDF contains random variables representing all chemical species at a particular spatial location. It can be found from the joint velocity, composition PDF by integrating over the entire phase space of the velocity components. The loss of instantaneous velocity information implies the following. 

In MC methods the ultimate objective is to evaluate macroscopic properties from information about molecular positions generated over phase space. To evaluate average macroscopic properties, p, in the canonical ensemble from statistical mechanics, the following expression is used ... 

Sample the initial distribution function P x,p,t = Q) by a set of random walkers. The ith random walker carries the information of its phase-space position (xi,pi), density-matrix component (nm)-, weight VF(xi, Pi, t), and phase a(xi, pi, t). Divide the whole time interval into N small enough pieces such that the desired accuracy of the Trotter scheme is guaranteed. 

We have recently introduced the Wigner intracule (2), a two-electron phase-space distribution. The Wigner intracule, W ( , v), is related to the probability of finding two electrons separated by a distance u and moving with relative momentum v. This reduced function provides a means to interpret the complexity of the wavefunction without removing all of the explicit multi-body information contained therein, as is the case in the one-electron density. 

The maximum entropy method (MEM) is an information-theory-based technique that was first developed in the field of radioastronomy to enhance the information obtained from noisy data (Gull and Daniell 1978). The theory is based on the same equations that are the foundation of statistical thermodynamics. Both the statistical entropy and the information entropy deal with the most probable distribution. In the case of statistical thermodynamics, this is the distribution of the particles over position and momentum space ( phase space ), while in the case of information theory, the distribution of numerical quantities over the ensemble of pixels is considered. 

For an arbitrary canonical density operator, the phase space centroid distribution fimction is imiquely defined. However, this function does not directly contain any dynamical information from the quantum ensemble because such information has been lost in the course of the trace operation. The lost information may be recovered by associating to each value of the centroid distribution function the following normalized operator ... 

The computational efficiency of a FF approach also enables simulations of dynamical behavior—molecular dynamics (MD). In MD, the classical equations of motion for a system of N atoms are solved to generate a search in phase space, or trajectory, under specified thermodynamic conditions (e.g., constant temperature or constant pressure). The trajectory provides configurational and momentum information for each atom from which thermodynamic properties such as the free energy, or time-dependent properties such as diffusion coefficients, can be calculated. 

At this point it is useful to conduct a thought experiment. Consider a system for which the only possible measurement is by a Stern-Gerlach machine oriented along the z-axis. In other words, assume that once the probability for coming out of the machine spin up is known, every physically predictable feature of the state is known. Then the pair (c+, c ) e would contain more information than is necessary. Only c+p and c P would have physical meaning, and because of the condition Ic+I - - c 1 = 1, even these two real numbers are dependent. Thus the phase space of this hypothetical system... 

Transition state theory (TST) (4) is a well-known method used to calculate the kinetics of infrequent events. The rate constant of the process of interest may be factored into two terms, a TST rate constant based on a knowledge of an equilibrium phase space distribution of the system, and a dynamical correction factor (close to unity) used to correct for errors in the TST rate constant. The correction factor can be evaluated from dynamical information obtained over a short time scale. 

Remark. We assumed that Y(t) is a Markov process. Usually, however, one is interested in materials in which a memory effect is present, because that provides more information about the microscopic magnetic moments and their interaction. In that case the above results are still formally correct, but the following qualification must be borne in mind. It is still true that p y0) is the distribution of Y at the time t0, at which the small field B is switched off. However, it is no longer true that this p(y0) uniquely specifies a subensemble and thereby the future of Y(t). It is now essential to know that the system has aged in the presence of B + AB, so that its density in phase space is canonical, not only with respect to Y, but also with respect to all other quantities that determine the future. Hence the formulas cannot be applied to time-dependent fields B(t) unless the variation is so slow that the system is able to maintain at all times the equilibrium distribution corresponding to the instantaneous B(t). 

The stability matrix carries the necessary information related to the vicinity of the trajectory and provides an efficient numerical procedure for computing the response function. It plays an important role in the field of classical chaos the sign of its eigenvalues (related to the Lyapunov exponents) controls the chaotic nature of the system. Interference effects in classical response functions have a different origin than their quantum counterparts. For each initial phase-space point we need to launch two trajectories with very close initial conditions. [For 5(n) we need n trajectories.] The nonlinear response is obtained by adding the contributions of these trajectories and letting them interfere. 

A statistical analysis of the fluctuational trajectories is based on the measurements of the prehistory probability distribution [60] ph(q, t qy, tf) (see Section IIIC). By investigating the prehistory probability distribution experimentally, one can establish the area of phase space within which optimal paths are well defined, specifically, where the tube of fluctuational paths around an optimal path is narrow. The prehistory distribution thus provides information about both the optimal path and the probability that it will be followed. In practice the method essentially reduces to continuously following the dynamics of the system and constructing the distribution of all realizations of the fluctuational trajectories that transfer it from a state of equilibrium to a prescribed remote state. 

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