Search results for "Taylor series"

showing 10 items of 30 documents

A taylor series model to evaluate the interelemental effects in X-ray fluorescence analysis, applied to the iron-zirconium-diluent system

1995

A semi-empirical model has been developed to quantify the interelemental effects in X-ray fluorescence analysis. The measured X-ray fluorescence intensity has been expressed as a function of the different fluorescence elements composing the sample. this complex function has become an operative function via a Taylor series development. An explication has been given for the significance of the different terms of the series. These terms respond to mathematical functions known as characteristic functions for each chemical system. A parameter (B) has been defined which makes it possible to quantify the influence of the interelemental effect as a function of the analyte concentration (C) and that…

AnalyteZirconiumSeries (mathematics)Characteristic function (probability theory)ChemistryAnalytical chemistryX-ray fluorescencechemistry.chemical_elementFunction (mathematics)BiochemistryFluorescenceAnalytical Chemistrysymbols.namesakeTaylor seriessymbolsFresenius' Journal of Analytical Chemistry
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Melnikov functions and Bautin ideal

2001

The computation of the number of limit cycles which appear in an analytic unfolding of planar vector fields is related to the decomposition of the displacement function of this unfolding in an ideal of functions in the parameter space, called the Ideal of Bautin. On the other hand, the asymptotic of the displacement function, for 1-parameter unfoldings of hamiltonian vector fields is given by Melnikov functions which are defined as the coefficients of Taylor expansion in the parameter. It is interesting to compare these two notions and to study if the general estimations of the number of limit cycles in terms of the Bautin ideal could be reduced to the computations of Melnikov functions for…

Applied MathematicsComputationMathematical analysisPlanar vector fieldsParameter spacesymbols.namesakeDisplacement functionTaylor seriessymbolsDiscrete Mathematics and CombinatoricsVector fieldHamiltonian (quantum mechanics)Melnikov methodMathematicsQualitative Theory of Dynamical Systems
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On the use of fractional calculus for the probabilistic characterization of random variables

2009

In this paper, the classical problem of the probabilistic characterization of a random variable is re-examined. A random variable is usually described by the probability density function (PDF) or by its Fourier transform, namely the characteristic function (CF). The CF can be further expressed by a Taylor series involving the moments of the random variable. However, in some circumstances, the moments do not exist and the Taylor expansion of the CF is useless. This happens for example in the case of $\alpha$--stable random variables. Here, the problem of representing the CF or the PDF of random variables (r.vs) is examined by introducing fractional calculus. Two very remarkable results are o…

Characteristic function (probability theory)FOS: Physical sciencesAerospace EngineeringMathematics - Statistics TheoryOcean EngineeringProbability density functionComplex order momentStatistics Theory (math.ST)Fractional calculusymbols.namesakeIngenieurwissenschaftenFOS: MathematicsTaylor seriesApplied mathematicsCharacteristic function serieMathematical PhysicsCivil and Structural EngineeringMathematicsGeneralized Taylor serieMechanical EngineeringStatistical and Nonlinear PhysicsProbability and statisticsMathematical Physics (math-ph)Condensed Matter PhysicsFractional calculusFourier transformNuclear Energy and EngineeringPhysics - Data Analysis Statistics and ProbabilitysymbolsFractional calculus; Generalized Taylor series; Complex order moments; Fractional moments; Characteristic function series; Probability density function seriesddc:620Series expansionFractional momentProbability density function seriesSettore ICAR/08 - Scienza Delle CostruzioniRandom variableData Analysis Statistics and Probability (physics.data-an)
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A method for the probabilistic analysis of nonlinear systems

1995

Abstract The probabilistic description of the response of a nonlinear system driven by stochastic processes is usually treated by means of evaluation of statistical moments and cumulants of the response. A different kind of approach, by means of new quantities here called Taylor moments, is proposed. The latter are the coefficients of the Taylor expansion of the probability density function and the moments of the characteristic function too. Dual quantities with respect to the statistical cumulants, here called Taylor cumulants, are also introduced. Along with the basic scheme of the method some illustrative examples are analysed in detail. The examples show that the proposed method is an a…

Characteristic function (probability theory)Stochastic processMechanical EngineeringAerospace EngineeringOcean EngineeringStatistical and Nonlinear PhysicsProbability density functionCondensed Matter Physicssymbols.namesakeNonlinear systemNuclear Energy and EngineeringTaylor seriessymbolsCalculusApplied mathematicsProbabilistic analysis of algorithmsCumulantCivil and Structural EngineeringMathematicsTaylor expansions for the moments of functions of random variables
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Stochastic analysis of dynamical systems with delayed control forces

2006

Abstract Reduction of structural vibration in actively controlled dynamical system is usually performed by means of convenient control forces dependent of the dynamic response. In this paper the existent studies will be extended to dynamical systems subjected to non-normal delta-correlated random process with delayed control forces. Taylor series expansion of the control forces has been introduced and the statistics of the dynamical response have been obtained by means of the extended Ito differential rule. Numerical application provided shows the capabilities of the proposed method to analyze stochastic dynamic systems with delayed actions under delta-correlated process contrasting statist…

Computational methods in classical mechanicNumerical AnalysisDynamical systems theoryStochastic processApplied MathematicsStochastic analysis methodsProcess (computing)General linear dynamical systemDynamical systemLinear dynamical systemsymbols.namesakeControl theoryModeling and SimulationTaylor seriessymbolsNonlinear dynamics and nonlinear dynamical systemDifferential (infinitesimal)Reduction (mathematics)MathematicsCommunications in Nonlinear Science and Numerical Simulation
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Highlighting numerical insights of an efficient SPH method

2018

Abstract In this paper we focus on two sources of enhancement in accuracy and computational demanding in approximating a function and its derivatives by means of the Smoothed Particle Hydrodynamics method. The approximating power of the standard method is perceived to be poor and improvements can be gained making use of the Taylor series expansion of the kernel approximation of the function and its derivatives. The modified formulation is appealing providing more accurate results of the function and its derivatives simultaneously without changing the kernel function adopted in the computation. The request for greater accuracy needs kernel function derivatives with order up to the desidered …

Computer scienceApplied MathematicsGaussianComputation010103 numerical & computational mathematicsFunction (mathematics)01 natural sciences010101 applied mathematicsSmoothed-particle hydrodynamicsComputational Mathematicssymbols.namesakeSettore MAT/08 - Analisi NumericaKernel based methods Smoothed Particle Hydrodynamics Accuracy Convergence Improved fast Gaussian transform.Convergence (routing)symbolsTaylor seriesGaussian function0101 mathematicsFocus (optics)Algorithm
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On the integration of kinetic models using a high-order taylor series method

1992

A general equation to derive kinetic models up to any order is given. This equation greatly facilitates the application of the Taylor series method to the integration of kinetic models up to very high orders. When dealing with non-stiff models, computing time is always reduced by increasing the integration order, at least up to the 20th order. When the model is stiff, the integration order should be optimized; however, a twelfth order is recommended to integrate weakly stiff models. The use of an algorithm which permits the immediate calculation of the integration step size required to maintain a given accuracy leads to further reductions in computing time. When implemented as recommended h…

Kinetic modelComputer programApplied MathematicsKinetic energyAnalytical Chemistrysymbols.namesakeOrder (business)General equationTaylor seriessymbolsOptimization methodsTaylor series methodAlgorithmMathematicsJournal of Chemometrics
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Comparison among three boundary element methods for torsion problems: CPM, CVBEM, LEM

2011

This paper provides solutions for De Saint-Venant torsion problem on a beam with arbitrary and uniform cross-section. In particular three methods framed into complex analysis have been considered: Complex Polynomial Method (CPM), Complex Variable Boundary Element Method (CVBEM) and Line Element-less Method (LEM), recently proposed. CPM involves the expansion of a complex potential in Taylor series, computing the unknown coefficients by means of collocation points on the boundary. CVBEM takes advantage of Cauchy’s integral formula that returns the solution of Laplace equation when mixed boundary conditions on both real and imaginary parts of the complex potential are known. LEM introduces th…

Laplace's equationApplied MathematicsLaurent seriesGeneral EngineeringCauchy distributionGeometryBoundary Element Methods Complex analysis Torsion.Computational Mathematicssymbols.namesakeCollocation methodTaylor seriessymbolsShear stressApplied mathematicsBoundary value problemSettore ICAR/08 - Scienza Delle CostruzioniBoundary element methodAnalysisMathematicsEngineering Analysis with Boundary Elements
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Method to find the Minimum 1D Linear Gradient Model for Seismic Tomography

2016

The changes in the state of a geophysical medium before a strong earthquake can be found by studying of 3D seismic velocity images constructed for consecutive time windows. A preliminary step is to see changes with time in a minimum 1D model. In this paper we develop a method that finds the parameters of the minimum linear gradient model by applying a two-dimensional Taylor series of the observed data for the seismic ray and by performing least-square minimization for all seismic rays. This allows us to obtain the mean value of the discrete observed variable, close to zero value.

Local earthquake tomography02 engineering and technology010502 geochemistry & geophysics01 natural sciencesTheoretical Computer SciencePhysics::Geophysicssymbols.namesakeTime windowsLinear gradient of velocity0202 electrical engineering electronic engineering information engineeringTaylor series0105 earth and related environmental sciencesAlgebra and Number TheoryZero (complex analysis)State (functional analysis)GeodesyLinear gradientVariable (computer science)Computational Theory and MathematicsLíkönSeismic tomographysymbols020201 artificial intelligence & image processingMinificationJarðskjálftarMinimum 1D modelGeologyJarðskjálftamælingarInformation Systems
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Infrared-finite algorithms in QED II. The expansion of the groundstate of an atom interacting with the quantized radiation field

2009

Abstract In this paper, we present an explicit and constructive algorithm enabling us to calculate the groundstate and the groundstate energy of a non-relativistic atom minimally coupled to the quantized radiation field up to an error of arbitrary finite order in the fine structure constant. Because of infrared divergences, which invalidate a straightforward Taylor expansion, an iterative construction is employed to remove the infrared cut-off in photon momentum space and to produce a convergent algorithm.

Mathematics(all)PhotonInfraredGeneral MathematicsAtom (order theory)Order (ring theory)Position and momentum spaceFine-structure constantsymbols.namesakeQuantum mechanicsTaylor seriessymbolsAlgorithmEnergy (signal processing)MathematicsAdvances in Mathematics
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