Twist phase in Gaussian-beam optics

The recently discovered twist phase is studied in the context of the full ten-parameter family of partially coherent general anisotropic Gaussian Schell-model beams. It is shown that the nonnegativity requirement on the cross-spectral density of the beam demands that the strength of the twist phase be bounded from above by the inverse of the transverse coherence area of the beam. The twist phase as a two-point function is shown to have the structure of the generalized Huygens kernel or Green's function of a first-order system. The ray-transfer matrix of this system is exhibited. Wolf-type coherent-mode decomposition of the twist phase is carried out. Imposition of the twist phase on an otherwise untwisted beam is shown to result in a linear transformation in the ray phase space of the Wigner distribution. Though this transformation preserves the four-dimensional phase-space volume, it is not symplectic and hence it can, when impressed on a Wigner distribution, push it out of the convex set of all bona fide Wigner distributions unless the original Wigner distribution was sufficiently deep into the interior of the set.

Wigner distributions for finite-state systems without redundant phase-point operators

We set up Wigner distributions for N-state quantum systems following a Dirac-inspired approach. In contrast to much of the work in this study, requiring a 2N x 2N phase space, particularly when N is even, our approach is uniformly based on an N x N phase-space grid and thereby avoids the necessity of having to invoke a `quadrupled' phase space and hence the attendant redundance. Both N odd and even cases are analysed in detail and it is found that there are striking differences between the two. While the N odd case permits full implementation of the marginal property, the even case does so only in a restricted sense. This has the consequence that in the even case one is led to several equally good definitions of the Wigner distributions as opposed to the odd case where the choice turns out to be unique.

Nonlinear ensemble prediction of chaotic daily rainfall

The significance of treating rainfall as a chaotic system instead of a stochastic system for a better understanding of the underlying dynamics has been taken up by various studies recently. However, an important limitation of all these approaches is the dependence on a single method for identifying the chaotic nature and the parameters involved. Many of these approaches aim at only analyzing the chaotic nature and not its prediction. In the present study, an attempt is made to identify chaos using various techniques and prediction is also done by generating ensembles in order to quantify the uncertainty involved. Daily rainfall data of three regions with contrasting characteristics (mainly in the spatial area covered), Malaprabha, Mahanadi and All-India for the period 1955-2000 are used for the study. Auto-correlation and mutual information methods are used to determine the delay time for the phase space reconstruction. Optimum embedding dimension is determined using correlation dimension, false nearest neighbour algorithm and also nonlinear prediction methods. The low embedding dimensions obtained from these methods indicate the existence of low dimensional chaos in the three rainfall series. Correlation dimension method is done on th phase randomized and first derivative of the data series to check whether the saturation of the dimension is due to the inherent linear correlation structure or due to low dimensional dynamics. Positive Lyapunov exponents obtained prove the exponential divergence of the trajectories and hence the unpredictability. Surrogate data test is also done to further confirm the nonlinear structure of the rainfall series. A range of plausible parameters is used for generating an ensemble of predictions of rainfall for each year separately for the period 1996-2000 using the data till the preceding year. For analyzing the sensitiveness to initial conditions, predictions are done from two different months in a year viz., from the beginning of January and June. The reasonably good predictions obtained indicate the efficiency of the nonlinear prediction method for predicting the rainfall series. Also, the rank probability skill score and the rank histograms show that the ensembles generated are reliable with a good spread and skill. A comparison of results of the three regions indicates that although they are chaotic in nature, the spatial averaging over a large area can increase the dimension and improve the predictability, thus destroying the chaotic nature. (C) 2010 Elsevier Ltd. All rights reserved.

Embracing Integrability in Stellar and Planetary Dynamics

Hamiltonian systems in stellar and planetary dynamics are typically near integrable. For example, Solar System planets are almost in two-body orbits, and in simulations of the Galaxy, the orbits of stars seem regular. For such systems, sophisticated numerical methods can be developed through integrable approximations. Following this theme, we discuss three distinct problems. We start by considering numerical integration techniques for planetary systems. Perturbation methods (that utilize the integrability of the two-body motion) are preferred over conventional "blind" integration schemes. We introduce perturbation methods formulated with Cartesian variables. In our numerical comparisons, these are superior to their conventional counterparts, but, by definition, lack the energy-preserving properties of symplectic integrators. However, they are exceptionally well suited for relatively short-term integrations in which moderately high positional accuracy is required. The next exercise falls into the category of stability questions in solar systems. Traditionally, the interest has been on the orbital stability of planets, which have been quantified, e.g., by Liapunov exponents. We offer a complementary aspect by considering the protective effect that massive gas giants, like Jupiter, can offer to Earth-like planets inside the habitable zone of a planetary system. Our method produces a single quantity, called the escape rate, which characterizes the system of giant planets. We obtain some interesting results by computing escape rates for the Solar System. Galaxy modelling is our third and final topic. Because of the sheer number of stars (about 10^11 in Milky Way) galaxies are often modelled as smooth potentials hosting distributions of stars. Unfortunately, only a handful of suitable potentials are integrable (harmonic oscillator, isochrone and Stäckel potential). This severely limits the possibilities of finding an integrable approximation for an observed galaxy. A solution to this problem is torus construction; a method for numerically creating a foliation of invariant phase-space tori corresponding to a given target Hamiltonian. Canonically, the invariant tori are constructed by deforming the tori of some existing integrable toy Hamiltonian. Our contribution is to demonstrate how this can be accomplished by using a Stäckel toy Hamiltonian in ellipsoidal coordinates.

E. C. G. Sudarshan and Symmetry in Classical Dynamics, Optics and Quantum Mechanics

We review work initiated and inspired by Sudarshan in relativistic dynamics, beam optics, partial coherence theory, Wigner distribution methods, multimode quantum optical squeezing, and geometric phases. The 1963 No Interaction Theorem using Dirac's instant form and particle World Line Conditions is recalled. Later attempts to overcome this result exploiting constrained Hamiltonian theory, reformulation of the World Line Conditions and extending Dirac's formalism, are reviewed. Dirac's front form leads to a formulation of Fourier Optics for the Maxwell field, determining the actions of First Order Systems (corresponding to matrices of Sp(2,R) and Sp(4,R)) on polarization in a consistent manner. These groups also help characterize properties and propagation of partially coherent Gaussian Schell Model beams, leading to invariant quality parameters and the new Twist phase. The higher dimensional groups Sp(2n,R) appear in the theory of Wigner distributions and in quantum optics. Elegant criteria for a Gaussian phase space function to be a Wigner distribution, expressions for multimode uncertainty principles and squeezing are described. In geometric phase theory we highlight the use of invariance properties that lead to a kinematical formulation and the important role of Bargmann invariants. Special features of these phases arising from unitary Lie group representations, and a new formulation based on the idea of Null Phase Curves, are presented.

Weighted subspace methods and spatial smoothing: analysis and comparison

The effect of using a spatially smoothed forward-backward covariance matrix on the performance of weighted eigen-based state space methods/ESPRIT, and weighted MUSIC for direction-of-arrival (DOA) estimation is analyzed. Expressions for the mean-squared error in the estimates of the signal zeros and the DOA estimates, along with some general properties of the estimates and optimal weighting matrices, are derived. A key result is that optimally weighted MUSIC and weighted state-space methods/ESPRIT have identical asymptotic performance. Moreover, by properly choosing the number of subarrays, the performance of unweighted state space methods can be significantly improved. It is also shown that the mean-squared error in the DOA estimates is independent of the exact distribution of the source amplitudes. This results in a unified framework for dealing with DOA estimation using a uniformly spaced linear sensor array and the time series frequency estimation problems.

Predictive uncertainty of chaotic daily streamflow using ensemble wavelet networks approach

Perfect or even mediocre weather predictions over a long period are almost impossible because of the ultimate growth of a small initial error into a significant one. Even though the sensitivity of initial conditions limits the predictability in chaotic systems, an ensemble of prediction from different possible initial conditions and also a prediction algorithm capable of resolving the fine structure of the chaotic attractor can reduce the prediction uncertainty to some extent. All of the traditional chaotic prediction methods in hydrology are based on single optimum initial condition local models which can model the sudden divergence of the trajectories with different local functions. Conceptually, global models are ineffective in modeling the highly unstable structure of the chaotic attractor. This paper focuses on an ensemble prediction approach by reconstructing the phase space using different combinations of chaotic parameters, i.e., embedding dimension and delay time to quantify the uncertainty in initial conditions. The ensemble approach is implemented through a local learning wavelet network model with a global feed-forward neural network structure for the phase space prediction of chaotic streamflow series. Quantification of uncertainties in future predictions are done by creating an ensemble of predictions with wavelet network using a range of plausible embedding dimensions and delay times. The ensemble approach is proved to be 50% more efficient than the single prediction for both local approximation and wavelet network approaches. The wavelet network approach has proved to be 30%-50% more superior to the local approximation approach. Compared to the traditional local approximation approach with single initial condition, the total predictive uncertainty in the streamflow is reduced when modeled with ensemble wavelet networks for different lead times. Localization property of wavelets, utilizing different dilation and translation parameters, helps in capturing most of the statistical properties of the observed data. The need for taking into account all plausible initial conditions and also bringing together the characteristics of both local and global approaches to model the unstable yet ordered chaotic attractor of a hydrologic series is clearly demonstrated.

New possibilities in amplitude- and phase-comparison techniques for distance relays

The paper reports further work on the amplitude-comparison technique described by the same authors in a previous paper. This technique is extended to develop improved polar characteristics. Discontinuous polar characteristics, like directional parallelograms, are obtained by a single measuring gate with a simple mode of relay circuitry, whereas two measuring gates are required to provide a directional-quadrilateral characteristic of potentially general application. The paper also describes some new possibilities in phase-comparison methods for distance-protection schemes. Comparator models which effect the amplitude and phase comparison of the relaying signals are described in their schematic form. A comprehensive theoretical basis for comparison is also presented.

Anomalous structural and dynamical phase transitions of soft colloidal binary mixtures

We report microscopic structural and dynamical measurements on binary mixtures of homopolymers and polymer grafted nanoparticles at high densities in good solvent. We find strong and unexpected anomalies in the structure and dynamics of these binary mixtures, including appearance of spontaneous orientational alignment, as a function of added homopolymers of different molecular weights. Our experiments point to the possibility of exploiting the phase space in density and homopolymer size, of such hybrid systems, to create new materials with novel structural and physical properties.

Anomalous phase transitions in soft colloid-polymer binary mixtures

We have shown earlier [1] that these PGNPs resemble star polymers or spherical brushes in terms of their morphology in the melt. However, these particles show dynamics in melt which is quite different from other soft colloidal particles. Since most of the work on soft colloidal particles have been performed in solutions we have now explored the phase behavior of the PGNPs in good solvent using microscopic structural and dynamical measurements on binary mixtures of homopolymers and soft colloids consisting of polymer grafted nanoparticles. We observe anomalous structural and dynamical phase transitions of these binary mixtures, including appearance of spontaneous orientational alignment and logarithmic structural relaxations, as a function of added homopolymers of different molecular weights. Our experiments points to the possibility of exploiting the phase space in density and homopolymer size, of such hybrid systems, to create new materials with unique properties.

Modelling of transient heat conduction with diffuse interface methods

We present a survey on different numerical interpolation schemes used for two-phase transient heat conduction problems in the context of interface capturing phase-field methods. Examples are general transport problems in the context of diffuse interface methods with a non-equal heat conductivity in normal and tangential directions to the interface. We extend the tonsorial approach recently published by Nicoli M et al (2011 Phys. Rev. E 84 1-6) to the general three-dimensional (3D) transient evolution equations. Validations for one-dimensional, two-dimensional and 3D transient test cases are provided, and the results are in good agreement with analytical and numerical reference solutions.

On variant distribution and coarsening behavior of the a phase in a metastable beta titanium alloy

The stereology, variant distribution and coarsening behavior of semicoherent alpha(hcp) precipitates in a beta(bcc) matrix of a Ti5553 alloy has been analyzed, and a dominant 3-variant cluster has been observed in which the variants are related to each other by an axis-angle pair <<11(2)over bar> 0 >/60 degrees. Shape and spatial distribution independent elastic self and interaction energies for all pairwise and triplet combinations of a have been calculated and it is found that the 3-cluster combination that is experimentally observed most frequently has the lowest energy for the semicoherent state. The coarsening behavior of the delta distribution follows LSW kinetics after an initial transient, and has been modeled by phase field methods. (C) 2016 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Estudo do comportamento caótico e determinação de dimensão fractal em modelos pré-inflacionários não compactos

O caos determinístico é um dos aspectos mais interessantes no que diz respeito à teoria moderna dos sistemas dinâmicos, e está intrinsecamente associado a pequenas variações nas condições iniciais de um dado modelo. Neste trabalho, é feito um estudo acerca do comportamento caótico em dois casos específicos. Primeiramente, estudam-se modelos préinflacionários não-compactos de Friedmann-Robertson-Walker com campo escalar minimamente acoplado e, em seguida, modelos anisotrópicos de Bianchi IX. Em ambos os casos, o componente material é um fluido perfeito. Tais modelos possuem constante cosmológica e podem ser estudados através de uma descrição unificada, a partir de transformações de variáveis convenientes. Estes sistemas possuem estruturas similares no espaço de fases, denominadas centros-sela, que fazem com que as soluções estejam contidas em hipersuperfícies cuja topologia é cilíndrica. Estas estruturas dominam a relação entre colapso e escape para a inflação, que podem ser tratadas como bacias cuja fronteira pode ser fractal, e que podem ser associadas a uma estrutura denominada repulsor estranho. Utilizando o método de contagem de caixas, são calculadas as dimensões características das fronteiras nos modelos, o que envolve técnicas e algoritmos de computação numérica, e tal método permite estudar o escape caótico para a inflação.

Aperfeiçoamento da previsão global em séries temporais caóticas

A previsão de valores futuros em séries temporais produzidas por sistemas caóticos pode ser aplicada em diversas áreas do conhecimento como Astronomia, Economia, Física, Medicina, Meteorologia e Oceanografia. O método empregado consiste na reconstrução do espaço de fase seguido de um termo de melhoria da previsão. As rotinas utilizadas para a previsão e análise nesta linha de pesquisa fazem parte do pacote TimeS, que apresenta resultados encorajadores nas suas aplicações. O aperfeiçoamento das rotinas computacionais do pacote com vistas à melhoria da acurácia obtida e à redução do tempo computacional é construído a partir da investigação criteriosa da minimização empregada na obtenção do mapa global. As bases matemáticas são estabelecidas e novas rotinas computacionais são criadas. São ampliadas as possibilidades de funções de ajuste que podem incluir termos transcendentais nos componentes dos vetores reconstruídos e também possuir termos lineares ou não lineares nos parâmetros de ajuste. O ganho de eficiência atingido permite a realização de previsões e análises que respondem a perguntas importantes relacionadas ao método de previsão e ampliam a possibilidade de aplicações a séries reais.

Dynamical structure in paleoclimate data

Deterministic chaos in dynamical systems offers a new paradigm for understanding irregular fluctuations. The theory of chaotic dynamical systems includes methods that can test whether any given set of time series data, such as paleoclimate proxy data, are consistent with a deterministic interpretation. Paleoclimate data with annual resolution and absolute dating provide multiple channels of concurrent time series; these multiple time series can be treated as potential phase space coordinates to test whether interannual climate variability is deterministic. Dynamical structure tests which take advantage of such multichannel data are proposed and illustrated by application to a simple synthetic model of chaos, and to two paleoclimate proxy data series.