Dynamical cluster approximation: Nonlocal dynamics of correlated electron systems

We recently introduced the dynamical cluster approximation (DCA), a technique that includes short-ranged dynamical correlations in addition to the local dynamics of the dynamical mean-field approximation while preserving causality. The technique is based on an iterative self-consistency scheme on a finite-size periodic cluster. The dynamical mean-field approximation (exact result) is obtained by taking the cluster to a single site (the thermodynamic limit). Here, we provide details of our method, explicitly show that it is causal, systematic, Phi derivable, and that it becomes conserving as the cluster size increases. We demonstrate the DCA by applying it to a quantum Monte Carlo and exact enumeration study of the two-dimensional Falicov-Kimball model. The resulting spectral functions preserve causality, and the spectra and the charge-density-wave transition temperature converge quickly and systematically to the thermodynamic limit as the cluster size increases.

Critical micelle concentration from a lattice gas model

The temperature dependence of the critical micelle concentration (CMC) and a closed-loop coexistence curve are obtained, via Monte Carlo simulations, in the water surfactant limit of a two-dimensional version of a statistical mechanical model for micro-emulsions, The CMC and the coexistence curve reproduce various experimental trends as functions of the couplings. In the oil-surfactant limit, there is a conventional coexistence cure with an upper consolute point that allows for a region of three-phase coexistence between oil-rich, water-rich and microemulsion phases.

Simulation of magnetovolume effects in Fe65Ni35 nanoparticles

We compare magnetovolume effects in bulk and nanoparticles by performing Monte Carlo simulations of a spin-analogous model with coupled spatial and magnetic degrees of freedom and chemical disorder. We find that correlations between surface and bulk atoms lead with decreasing particle size to a substantial modification of the magnetic and elastic behavior at low temperatures.

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements m in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes' theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito-Taylor's discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplifiedmby considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations. (C) 2010 Elsevier Ltd. All rights reserved.

Instructional aid for tactical wargame model

A tactical gaming model for wargame play between two teams A and B through a control unit C has been developed, which can be handled using IBM personal computers (XT and AT models) having a local area network facility. This simulation model involves communication between the teams involved, logging and validation of the actions of the teams by the control unit. The validation procedure uses statistical and also monte carlo techniques. This model has been developed to evaluate the planning strategies of the teams involved. This application software using about 120 files has been developed in BASIC, DBASE and the associated network software. Experience gained in the instruction courses using this model will also be discussed.

Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.

Monte carlo simulation for molecular gas dynamics

The dynamics of low-density flows is governed by the Boltzmann equation of the kinetic theory of gases. This is a nonlinear integro-differential equation and, in general, numerical methods must be used to obtain its solution. The present paper, after a brief review of Direct Simulation Monte Carlo (DSMC) methods due to Bird, and Belotserkovskii and Yanitskii, studies the details of theDSMC method of Deshpande for mono as well as multicomponent gases. The present method is a statistical particle-in-cell method and is based upon the Kac-Prigogine master equation which reduces to the Boltzmann equation under the hypothesis of molecular chaos. The proposed Markoff model simulating the collisions uses a Poisson distribution for the number of collisions allowed in cells into which the physical space is divided. The model is then extended to a binary mixture of gases and it is shown that it is necessary to perform the collisions in a certain sequence to obtain unbiased simulation.

Risk minimization in water quality control problems of a river system

Methodologies are presented for minimization of risk in a river water quality management problem. A risk minimization model is developed to minimize the risk of low water quality along a river in the face of conflict among various stake holders. The model consists of three parts: a water quality simulation model, a risk evaluation model with uncertainty analysis and an optimization model. Sensitivity analysis, First Order Reliability Analysis (FORA) and Monte-Carlo simulations are performed to evaluate the fuzzy risk of low water quality. Fuzzy multiobjective programming is used to formulate the multiobjective model. Probabilistic Global Search Laussane (PGSL), a global search algorithm developed recently, is used for solving the resulting non-linear optimization problem. The algorithm is based on the assumption that better sets of points are more likely to be found in the neighborhood of good sets of points, therefore intensifying the search in the regions that contain good solutions. Another model is developed for risk minimization, which deals with only the moments of the generated probability density functions of the water quality indicators. Suitable skewness values of water quality indicators, which lead to low fuzzy risk are identified. Results of the models are compared with the results of a deterministic fuzzy waste load allocation model (FWLAM), when methodologies are applied to the case study of Tunga-Bhadra river system in southern India, with a steady state BOD-DO model. The fractional removal levels resulting from the risk minimization model are slightly higher, but result in a significant reduction in risk of low water quality. (c) 2005 Elsevier Ltd. All rights reserved.

A kinetic Ising model study of dynamical correlations in confined fluids: Emergence of both fast and slow time scales

Experiments and computer simulation studies have revealed existence of rich dynamics in the orientational relaxation of molecules in confined systems such as water in reverse micelles, cyclodextrin cavities, and nanotubes. Here we introduce a novel finite length one dimensional Ising model to investigate the propagation and the annihilation of dynamical correlations in finite systems and to understand the intriguing shortening of the orientational relaxation time that has been reported for small sized reverse micelles. In our finite sized model, the two spins at the two end cells are oriented in the opposite directions to mimic the effects of surface that in real system fixes water orientation in the opposite directions. This produces opposite polarizations to propagate inside from the surface and to produce bulklike condition at the center. This model can be solved analytically for short chains. For long chains, we solve the model numerically with Glauber spin flip dynamics (and also with Metropolis single-spin flip Monte Carlo algorithm). We show that model nicely reproduces many of the features observed in experiments. Due to the destructive interference among correlations that propagate from the surface to the core, one of the rotational relaxation time components decays faster than the bulk. In general, the relaxation of spins is nonexponential due to the interplay between various interactions. In the limit of strong coupling between the spins or in the limit of low temperature, the nature of relaxation of the spins undergoes a qualitative change with the emergence of a homogeneous dynamics where decay is predominantly exponential, again in agreement with experiments. (C) 2010 American Institute of Physics. doi: 10.1063/1.3474948]

Valaistusyhtälö

Tässä tietokonegrafiikan alan tutkielmassa tutkitaan annetun kohteen tai maailman valaistuksen määrittämistä laskennallisesti. Ilmiöt kuvataan fysikaalisesti ja valaistusta mallinnetaan ilmiöitä kuvaavilla yhtälöillä. Yhtälöiden ratkaisu perustuu valonsäteiden etenemisen ja sironnan laskennalliseen seurantaan. Näin johdettua matemaattista mallia tutkitaan ja yhtälöiden ratkaisut muotoillaan tietokoneella laskettavaan muotoon. Lopuksi ohjelmoidaan esiteltyä teoriaa hyödyntävä numeerinen ratkaisija ja esitellään käytetyt menetelmät ja tulokset. Valaistuksen matemaattista mallintamista lähestytään fysikaaliselta pohjalta. Aluksi esitellään valon luonnetta ja yhteyttä sähkömagneettiseen säteilyyn ja ihmisen näköjärjestelmään. Tämän jälkeen paneudutaan valonsäteiden heijastumiseen ja sirontaan ja yleistetään klassiset ideaaliset sirontamallit huomioimaan pinnan hienorakenne sirontajakaumilla. Tutkittu ongelma muotoillaan matemaattisiksi yhtälöiksi jotka ratkaistaan analyyttisesti. Ratkaisu osoittautuu intuitiiviseksi: valaistus saadaan laskemalla valonlähteistä emittoituvan valon kaikkien kertalukujen heijastukset. Analyyttinen ratkaisu muotoillaan rekursiiviseksi ja ratkaistaan todennäköisyyslaskentaan perustuvalla Monte Carlo -integroinnilla, jonka suppenemista nopeutetaan tärkeysotannalla. Numeerinen ratkaisu osoitetaan odotusarvoisesti harhattomaksi ja ratkaisun virheen osoitetaan puolittuvan laskentapisteiden määrän nelinkertaistuessa. Käytettävä todennäköisyyslaskenta esitellään pääpiirteittäin. Numeerinen ratkaisumenetelmä on stokastista säteenseurantaa yleistävä polunseuranta. Maailma määritellään kolmioverkkona ja pintojen normaalit annetaan kolmioiden kärkipisteissä. Kolmioista muodostetaan kuvaus tasopinnalle, josta voidaan tarvittaessa lukea esimerkiksi pinnan tarkemmat normaalit, sirontaominaisuudet tai absorptiospektri. Numeerisen ratkaisun eniten aikaa vievä osuus on valonsäteen seuraavan osumapisteen selvitys maailman pintojen välillä. Ratkaisua nopeutetaan tallentamalla maailman kolmiot tehokkaaseen tietorakenteeseen, kd-puuhun, joka mahdollistaa valonsäteen ja suurten kolmiojoukkojen nopeat leikkaustarkistukset. Kd-puun ajatus ja toteutus esitellään työssä lyhyesti. Lopuksi esitellään ratkaisun eri vaiheet ja teoria käytännössä ja nähdään konkreettisesti eri menetelmien merkitys numeerisen ratkaisijan tuottamaan kuvaan. Lisäksi esitellään tehokas prioriteettijonoon perustuva adaptiivinen menetelmä kuvaan jääneen kohinan pienentämiseksi tutkimalla näytteistyksen otoskeskihajonnan ja keskiarvon suhdetta kuvapisteittäin.

Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.

An efficient reduction algorithm for computation of interconnect delay variability for statistical timing analysis in clock tree planning

In this paper, we propose a novel and efficient algorithm for modelling sub-65 nm clock interconnect-networks in the presence of process variation. We develop a method for delay analysis of interconnects considering the impact of Gaussian metal process variations. The resistance and capacitance of a distributed RC line are expressed as correlated Gaussian random variables which are then used to compute the standard deviation of delay Probability Distribution Function (PDF) at all nodes in the interconnect network. Main objective is to find delay PDF at a cheaper cost. Convergence of this approach is in probability distribution but not in mean of delay. We validate our approach against SPICE based Monte Carlo simulations while the current method entails significantly lower computational cost.

Impact of energy quantisation in single electron transistor island on hybrid complementary metal oxide semiconductor-single electron transistor integrated circuits

For the first time, the impact of energy quantisation in single electron transistor (SET) island on the performance of hybrid complementary metal oxide semiconductor (CMOS)-SET transistor circuits has been studied. It has been shown through simple analytical models that energy quantisation primarily increases the Coulomb Blockade area and Coulomb Blockade oscillation periodicity of the SET device and thus influences the performance of hybrid CMOS-SET circuits. A novel computer aided design (CAD) framework has been developed for hybrid CMOS-SET co-simulation, which uses Monte Carlo (MC) simulator for SET devices along with conventional SPICE for metal oxide semiconductor devices. Using this co-simulation framework, the effects of energy quantisation have been studied for some hybrid circuits, namely, SETMOS, multiband voltage filter and multiple valued logic circuits. Although energy quantisation immensely deteriorates the performance of the hybrid circuits, it has been shown that the performance degradation because of energy quantisation can be compensated by properly tuning the bias current of the current-biased SET devices within the hybrid CMOS-SET circuits. Although this study is primarily done by exhaustive MC simulation, effort has also been put to develop first-order compact model for SET that includes energy quantisation effects. Finally, it has been demonstrated that one can predict the SET behaviour under energy quantisation with reasonable accuracy by slightly modifying the existing SET compact models that are valid for metallic devices having continuous energy states.

QCD with dynamical Wilson fermions. II

We present results for the QCD spectrum and the matrix elements of scalar and axial-vector densities at β=6/g2=5.4, 5.5, 5.6. The lattice update was done using the hybrid Monte Carlo algorithm to include two flavors of dynamical Wilson fermions. We have explored quark masses in the range ms≤mq≤3ms. The results for the spectrum are similar to quenched simulations and mass ratios are consistent with phenomenological heavy-quark models. The results for matrix elements of the scalar density show that the contribution of sea quarks is comparable to that of the valence quarks. This has important implications for the pion-nucleon σ term.