Search results for "Stochastic Proce"

showing 10 items of 349 documents

Roughness of two nonintersecting one-dimensional interfaces.

2006

The dynamics of two spatially discrete one-dimensional single-step model interfaces with a noncrossing constraint is studied in both nonsymmetric propagating and symmetric relaxing cases. We consider possible scaling scenarios and study a few special cases by using continuous-time Monte Carlo simulations. The roughness of the interfaces is observed to be nonmonotonic as a function of time, and in the stationary state it is nonmonotonic also as a function of the strength of the effective force driving the interfaces against each other. This is related on the one hand to the reduction of the available configuration space and on the other hand to the ability of the interfaces to conform to eac…

Stochastic processMonte Carlo methodStatistical physicsFunction (mathematics)Configuration spaceSurface finishReduction (mathematics)ScalingStationary stateMathematicsPhysical review. E, Statistical, nonlinear, and soft matter physics
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Stochastic analysis of motorcycle dynamics

2011

Off-road and racing motorcycles require a particular setup of the suspensions to improve the comfort and the safety of the rider, maintaining a continuous contact between the road and the motorcycle (by means of the tires). Further, because of the ground roughness, in the case of offroad motorcycle, suspensions usually experience extreme and erratic excursions (suspension stroke) in performing their function. In this regard, the adoption of nonlinear devices can, perhaps, limit both the acceleration experienced by the sprung mass and the excursions of the suspensions. This leads to the consideration of asymmetric nonlinearly-behaving suspensions. This option, however, induces the difficulty…

Stochastic processStatistical linearization Autoregressive models Monte Carlo simulation Nonlinear devices.Bicycle and motorcycle dynamicsStatistical physicsSettore ICAR/08 - Scienza Delle CostruzioniMathematics
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Univariate and multivariate statistical aspects of equity volatility

2004

We discuss univariate and multivariate statistical properties of volatility time series of equities traded in a financial market. Specifically, (i) we introduce a two-region stochastic volatility model able to well describe the unconditional pdf of volatility in a wide range of values and (ii) we quantify the stability of the results of a correlation-based clustering procedure applied to synchronous time evolution of a set of volatility time series.

Stochastic volatilityFinancial models with long-tailed distributions and volatility clusteringVolatility smileUnivariateEconometricsForward volatilityEconomicsVolatility (finance)Implied volatilitySettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)volatility financial markets econophysics log range correlated processes stochastic processesHeston model
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Set-valued stochastic integral equations driven by martingales

2012

Abstract We consider a notion of set-valued stochastic Lebesgue–Stieltjes trajectory integral and a notion of set-valued stochastic trajectory integral with respect to martingale. Then we use these integrals in a formulation of set-valued stochastic integral equations. The existence and uniqueness of the solution to such the equations is proven. As a generalization of set-valued case results we consider the fuzzy stochastic trajectory integrals and investigate the fuzzy stochastic integral equations driven by bounded variation processes and martingales.

Stratonovich integralContinuous-time stochastic processApplied MathematicsMathematical analysisMathematicsofComputing_NUMERICALANALYSISStochastic calculusRiemann–Stieltjes integralRiemann integralsymbols.namesakeQuantum stochastic calculusImproper integralsymbolsDaniell integralAnalysisMathematicsJournal of Mathematical Analysis and Applications
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The Itô Integral

2014

The Ito integral allows us to integrate stochastic processes with respect to the increments of a Brownian motion or a somewhat more general stochastic process. We develop the Ito integral first for Brownian motion and then for generalized diffusion processes (so called Ito processes). In the third section, we derive the celebrated Ito formula. This is the chain rule for the Ito integral that enables us to do explicit calculations with the Ito integral. In the fourth section, we use the Ito formula to obtain a stochastic solution of the classical Dirichlet problem. This in turn is used in the fifth section in order to show that like symmetric simple random walk, Brownian motion is recurrent …

Stratonovich integralDirichlet problemSection (fiber bundle)Mathematics::ProbabilityStochastic processMathematical analysisLocal martingaleChain ruleDiffusion (business)Brownian motionMathematics
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Fractional Viscoelasticity Under Combined Stress and Temperature Variations

2020

Nowadays polymeric materials or composites with polymeric matrices are widely used in a very wide range of applications such as aerospace, automotive, biomedical and also civil engineering. From a mechanical point of view, polymers are characterized by high viscoelastic properties and high sensitiveness of mechanical parameters from temperature. Analytical predictions in real-life conditions of mechanical behaviour of such a kind of materials is not trivial for the intrinsic hereditariness that imply the knowledge of all the history of the material at hand in order to predict the response to applied external loads. If temperature variations are also present in the materials, a reliable eval…

Stress (mechanics)symbols.namesakeWork (thermodynamics)Superposition principleMaterials scienceDiscretizationStochastic processMonte Carlo methodBoltzmann constantsymbolsMechanicsViscoelasticity
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Stochastic model for complex surface-reaction systems with application toNH3formation

1993

A stochastic model is introduced that is appropriate to describe surface-reaction systems. These reaction systems are well suited for the description via master equations using their Markovian behavior. In this representation an infinite chain of master equations for the distribution functions of the state of the surface, of pairs of surface sites, etc., arises. This hierarchy is truncated by a superposition approximation. The resulting lattice equations are solved in a small region which contains all of the structure-sensitive aspects and can be connected to continuous functions which represent the behavior of the system for large distances from a reference point. In the present paper, we …

Superposition principleContinuous-time stochastic processDistribution functionStochastic modellingLattice (order)Monte Carlo methodMaster equationDynamic Monte Carlo methodStatistical physicsMathematicsPhysical Review E
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Challenging aspects in Consensus protocols for networks

2008

Results on consensus protocols for networks are presented. The basic tools and the main contribution available in the literature are considered, together with some of the related challenging aspects: estimation in networks and how to deal with disturbances is considered. Motivated by applications to sensor, peer-to- peer, and ad hoc networks, many papers have considered the problem of estimation in a consensus fashion. Here, the unknown but bounded (UBB) noise affecting the network is addressed in details. Because of the presence of UBB disturbances convergence to equilibria with all equal components is, in general, not possible. The solution of the epsiv-consensus problem, where the states…

Theoretical computer scienceAutomatic controlConsensus problemsWireless ad hoc networkStochastic processEstimation theoryComputer scienceDistributed computingMulti-agent systemConsensus problems; Consensus protocolsConsensus protocolsBounded functionConvergence (routing)Wireless sensor network
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A solution to the stochastic point location problem in metalevel nonstationary environments.

2008

This paper reports the first known solution to the stochastic point location (SPL) problem when the environment is nonstationary. The SPL problem involves a general learning problem in which the learning mechanism (which could be a robot, a learning automaton, or, in general, an algorithm) attempts to learn a "parameter," for example, lambda*, within a closed interval. However, unlike the earlier reported results, we consider the scenario when the learning is to be done in a nonstationary setting. For each guess, the environment essentially informs the mechanism, possibly erroneously (i.e., with probability p), which way it should move to reach the unknown point. Unlike the results availabl…

Theoretical computer scienceAutomatic controlDiscretizationComputer scienceInformation Storage and RetrievalDecision Support TechniquesPattern Recognition AutomatedArtificial IntelligenceComputer SimulationElectrical and Electronic EngineeringStochastic ProcessesModels StatisticalLearning automatabusiness.industryStochastic processSignal Processing Computer-AssistedGeneral MedicineRandom walkComputer Science ApplicationsAutomatonHuman-Computer InteractionControl and Systems EngineeringPoint locationArtificial intelligencebusinessSoftwareAlgorithmsInformation SystemsIEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society
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Thermodynamic formalism and linear response theory for non-equilibrium steady states

2016

We study the linear response in systems driven away from thermal equilibrium into a nonequilibrium steady state with nonvanishing entropy production rate. A simple derivation of a general response formula is presented under the condition that the generating function describes a transformation that (to lowest order) preserves normalization and thus describes a physical stochastic process. For Markov processes we explicitly construct the conjugate quantities and discuss their relation with known response formulas. Emphasis is put on the formal analogy with thermodynamic potentials and some consequences are discussed.

Thermal equilibriumNormalization (statistics)Statistical Mechanics (cond-mat.stat-mech)Stochastic processEntropy productionMarkov processNon-equilibrium thermodynamicsFOS: Physical sciences01 natural sciences010305 fluids & plasmasThermodynamic potentialsymbols.namesake0103 physical sciencessymbolsStatistical physics010306 general physicsLinear response theoryCondensed Matter - Statistical MechanicsMathematics
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