The Annals of Statistics, ISSN 0090-5364, 4/2008, Volume 36, Issue 2, pp. 906 - 937

This paper provides closed-form expansions for the log-likelihood function of multivariate diffusions sampled at discrete time intervals. The coefficients of...

Maximum likelihood estimation | Approximation | Differential equations | Series expansion | Polynomials | Mathematical functions | Stochastic models | Coefficients | Diffusion coefficient | Parametric models | Diffusions | Discrete observations | Likelihood | Expansions | likelihood | TERM STRUCTURE | MODELS | CONTINGENT CLAIMS | STATISTICS & PROBABILITY | discrete observations | DISCRETELY SAMPLED DIFFUSIONS | APPROXIMATION APPROACH | diffusions | expansions | 60H10 | 60J60 | 62M05 | 62F12

Maximum likelihood estimation | Approximation | Differential equations | Series expansion | Polynomials | Mathematical functions | Stochastic models | Coefficients | Diffusion coefficient | Parametric models | Diffusions | Discrete observations | Likelihood | Expansions | likelihood | TERM STRUCTURE | MODELS | CONTINGENT CLAIMS | STATISTICS & PROBABILITY | discrete observations | DISCRETELY SAMPLED DIFFUSIONS | APPROXIMATION APPROACH | diffusions | expansions | 60H10 | 60J60 | 62M05 | 62F12

Journal Article

SIAM Journal on Numerical Analysis, ISSN 0036-1429, 1/2014, Volume 52, Issue 6, pp. 2599 - 2622

In this paper, a new alternating direction implicit Galerkin–Legendre spectral method for the two-dimensional Riesz space fractional nonlinear...

Hypergeometric functions | Error rates | Approximation | Porous materials | Numerical methods | Differential equations | Reaction diffusion equations | Legendre polynomials | Spectral methods | Finite difference methods | Riesz space fractional reaction-diffusion equation | Fractional FitzHugh-Nagumo model | Alternating direction implicit method | Legendre spectral method | Stability and convergence | FOKKER-PLANCK EQUATION | stability and convergence | MATHEMATICS, APPLIED | fractional FitzHugh-Nagumo model | STABILITY | GALERKIN METHOD | FINITE-DIFFERENCE METHOD | SUBDIFFUSION EQUATION | alternating direction implicit method | NUMERICAL APPROXIMATION | ANOMALOUS DIFFUSION | SOURCE-TERM | CONVERGENCE | ADVECTION-DISPERSION | Nodular iron | Nonlinearity | Mathematical models | Reaction-diffusion equations | Two dimensional | Galerkin methods | Convergence

Hypergeometric functions | Error rates | Approximation | Porous materials | Numerical methods | Differential equations | Reaction diffusion equations | Legendre polynomials | Spectral methods | Finite difference methods | Riesz space fractional reaction-diffusion equation | Fractional FitzHugh-Nagumo model | Alternating direction implicit method | Legendre spectral method | Stability and convergence | FOKKER-PLANCK EQUATION | stability and convergence | MATHEMATICS, APPLIED | fractional FitzHugh-Nagumo model | STABILITY | GALERKIN METHOD | FINITE-DIFFERENCE METHOD | SUBDIFFUSION EQUATION | alternating direction implicit method | NUMERICAL APPROXIMATION | ANOMALOUS DIFFUSION | SOURCE-TERM | CONVERGENCE | ADVECTION-DISPERSION | Nodular iron | Nonlinearity | Mathematical models | Reaction-diffusion equations | Two dimensional | Galerkin methods | Convergence

Journal Article

Journal of Computational Physics, ISSN 0021-9991, 07/2019, Volume 388, pp. 209 - 223

In this paper, Runge–Kutta–Gegenbauer ( RKG) stability polynomials of arbitrarily high order of accuracy are introduced in closed form. The stability domain of...

Stability and convergence of numerical methods | Method of lines | Stiff equations | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PHYSICS, MATHEMATICAL | Methods | Algorithms | Advection | Runge-Kutta method | Polynomials | Stability

Stability and convergence of numerical methods | Method of lines | Stiff equations | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | PHYSICS, MATHEMATICAL | Methods | Algorithms | Advection | Runge-Kutta method | Polynomials | Stability

Journal Article

Annals of Applied Probability, ISSN 1050-5164, 08/2018, Volume 28, Issue 4, pp. 2451 - 2500

Polynomial jump-diffusions constitute a class of tractable stochastic models with wide applicability in areas such as mathematical finance and population...

Stochastic models with jumps | Wright–Fisher diffusion | Stochastic invariance | Unit simplex | Polynomial processes | unit simplex | ONE-DIMENSION | stochastic models with jumps | Wright-Fisher diffusion | STATISTICS & PROBABILITY | MOMENTUM PROBLEM | stochastic invariance

Stochastic models with jumps | Wright–Fisher diffusion | Stochastic invariance | Unit simplex | Polynomial processes | unit simplex | ONE-DIMENSION | stochastic models with jumps | Wright-Fisher diffusion | STATISTICS & PROBABILITY | MOMENTUM PROBLEM | stochastic invariance

Journal Article

ELECTRONIC JOURNAL OF PROBABILITY, ISSN 1083-6489, 2019, Volume 24

We introduce a class of probability measure-valued diffusions, coined polynomial, of which the well-known Fleming-Viot process is a particular example. The...

probability measure-valued processes | martingale problem | dual process | interacting particle systems | polynomial processes | STATISTICS & PROBABILITY | Fleming-Viot type processes | maximum principle

probability measure-valued processes | martingale problem | dual process | interacting particle systems | polynomial processes | STATISTICS & PROBABILITY | Fleming-Viot type processes | maximum principle

Journal Article

BIOMETRIKA, ISSN 0006-3444, 12/2019, Volume 106, Issue 4, pp. 941 - 956

We develop and implement a novel M-estimation method for locally stationary diffusions observed at discrete time-points. We give sufficient conditions for the...

Martingale estimating function | MAXIMUM-LIKELIHOOD-ESTIMATION | TERM STRUCTURE | MODELS | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | TIME-SERIES | Kernel estimation | STATISTICS & PROBABILITY | POLYNOMIAL DIFFUSIONS | Biological signal | Polynomial diffusion

Martingale estimating function | MAXIMUM-LIKELIHOOD-ESTIMATION | TERM STRUCTURE | MODELS | BIOLOGY | MATHEMATICAL & COMPUTATIONAL BIOLOGY | TIME-SERIES | Kernel estimation | STATISTICS & PROBABILITY | POLYNOMIAL DIFFUSIONS | Biological signal | Polynomial diffusion

Journal Article

Finance and Stochastics, ISSN 0949-2984, 10/2016, Volume 20, Issue 4, pp. 931 - 972

This paper provides the mathematical foundation for polynomial diffusions. They play an important role in a growing range of applications in finance, including...

Polynomial diffusions | 60H30 | 60J60 | Economic Theory/Quantitative Economics/Mathematical Methods | G13 | Probability Theory and Stochastic Processes | G12 | Mathematics | Stochastic invariance | Quantitative Finance | Finance, general | Statistics for Business/Economics/Mathematical Finance/Insurance | Boundary attainment | Moment problem | Polynomial diffusion models in finance | MULTIDIMENSIONAL MOMENT PROBLEM | EXCHANGE-RATES | PROPERTY | STATISTICS & PROBABILITY | BUSINESS, FINANCE | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MODELS | SOCIAL SCIENCES, MATHEMATICAL METHODS | Financial markets | Interest rates

Polynomial diffusions | 60H30 | 60J60 | Economic Theory/Quantitative Economics/Mathematical Methods | G13 | Probability Theory and Stochastic Processes | G12 | Mathematics | Stochastic invariance | Quantitative Finance | Finance, general | Statistics for Business/Economics/Mathematical Finance/Insurance | Boundary attainment | Moment problem | Polynomial diffusion models in finance | MULTIDIMENSIONAL MOMENT PROBLEM | EXCHANGE-RATES | PROPERTY | STATISTICS & PROBABILITY | BUSINESS, FINANCE | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MODELS | SOCIAL SCIENCES, MATHEMATICAL METHODS | Financial markets | Interest rates

Journal Article

Econometrica, ISSN 0012-9682, 1/2002, Volume 70, Issue 1, pp. 223 - 262

When a continuous-time diffusion is observed only at discrete dates, in most cases the transition distribution and hence the likelihood function of the...

Brownian motion | Maximum likelihood estimation | Series convergence | Estimate reliability | Approximation | Infinity | Polynomials | Hermite polynomials | Estimators | Perceptron convergence procedure | continuous‐time diffusion | discrete sampling | maximum‐likelihood estimation | transition density | Hermite expansion | Transition density | Maximum-likelihood estimation | Continuous-time diffusion | Discrete sampling | TRANSITION DENSITIES | MARKET | continuous-time diffusion | STATISTICS & PROBABILITY | maximum-likelihood estimation | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | TERM STRUCTURE | MODELS | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | Usage | Models | Econometrics | Maximum likelihood estimates (Statistics) | Analysis | Closure of functions | Studies | Economic models | Statistical analysis | Sampling | Monte Carlo simulation

Brownian motion | Maximum likelihood estimation | Series convergence | Estimate reliability | Approximation | Infinity | Polynomials | Hermite polynomials | Estimators | Perceptron convergence procedure | continuous‐time diffusion | discrete sampling | maximum‐likelihood estimation | transition density | Hermite expansion | Transition density | Maximum-likelihood estimation | Continuous-time diffusion | Discrete sampling | TRANSITION DENSITIES | MARKET | continuous-time diffusion | STATISTICS & PROBABILITY | maximum-likelihood estimation | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | TERM STRUCTURE | MODELS | SOCIAL SCIENCES, MATHEMATICAL METHODS | ECONOMICS | Usage | Models | Econometrics | Maximum likelihood estimates (Statistics) | Analysis | Closure of functions | Studies | Economic models | Statistical analysis | Sampling | Monte Carlo simulation

Journal Article

Stochastic Processes and their Applications, ISSN 0304-4149, 03/2017, Volume 127, Issue 3, pp. 901 - 926

Polynomial processes are defined by the property that conditional expectations of polynomial functions of the process are again polynomials of the same or...

Pathwise uniqueness | Sums of squares | Stochastic invariance | Smooth densities | Polynomial diffusion | Biquadratic forms | MODELS | SEMIDEFINITE BIQUADRATIC FORMS | STATISTICS & PROBABILITY | UNIQUENESS | Stochastic processes | Analysis

Pathwise uniqueness | Sums of squares | Stochastic invariance | Smooth densities | Polynomial diffusion | Biquadratic forms | MODELS | SEMIDEFINITE BIQUADRATIC FORMS | STATISTICS & PROBABILITY | UNIQUENESS | Stochastic processes | Analysis

Journal Article

Applied Mathematics and Computation, ISSN 0096-3003, 03/2017, Volume 296, pp. 1 - 17

•Operational matrix of shifted Jacobi polynomials is considered.•Solution of time-fractional order convection–diffusion problem is numerically estimated.•Main...

Jacobi polynomials | Time-fractional convection–diffusion equation | Caputo fractional derivative | OPERATIONAL MATRIX | MATHEMATICS, APPLIED | Time-fractional convection-diffusion equation | DIFFERENTIAL-EQUATIONS | TERM | HOMOTOPY ANALYSIS METHOD | Differential equations

Jacobi polynomials | Time-fractional convection–diffusion equation | Caputo fractional derivative | OPERATIONAL MATRIX | MATHEMATICS, APPLIED | Time-fractional convection-diffusion equation | DIFFERENTIAL-EQUATIONS | TERM | HOMOTOPY ANALYSIS METHOD | Differential equations

Journal Article

SIAM Journal on Numerical Analysis, ISSN 0036-1429, 1/2014, Volume 52, Issue 5, pp. 2512 - 2529

We propose and analyze a time-stepping discontinuous Petrov–Galerkin method combined with the continuous conforming finite element method in space for the...

Error rates | Cauchy Schwarz inequality | Spatial variability | Mathematical discontinuity | Approximation | Error analysis | Numerical methods | Uniqueness | Polynomials | Finite difference methods | Stability and error analysis | Fractional diffusion | Variable time steps | Discontinuous Petrov-Galerkin method | MATHEMATICS, APPLIED | variable time steps | DISCRETIZATION | fractional diffusion | NUMERICAL-METHOD | discontinuous Petrov-Galerkin method | DIRECTION IMPLICIT SCHEMES | STABILITY | FINITE-DIFFERENCE METHOD | stability and error analysis | Errors | Numerical analysis | Discretization | Mathematical analysis | Exact solutions | Mathematical models | Convergence

Error rates | Cauchy Schwarz inequality | Spatial variability | Mathematical discontinuity | Approximation | Error analysis | Numerical methods | Uniqueness | Polynomials | Finite difference methods | Stability and error analysis | Fractional diffusion | Variable time steps | Discontinuous Petrov-Galerkin method | MATHEMATICS, APPLIED | variable time steps | DISCRETIZATION | fractional diffusion | NUMERICAL-METHOD | discontinuous Petrov-Galerkin method | DIRECTION IMPLICIT SCHEMES | STABILITY | FINITE-DIFFERENCE METHOD | stability and error analysis | Errors | Numerical analysis | Discretization | Mathematical analysis | Exact solutions | Mathematical models | Convergence

Journal Article

Journal of Computational Physics, ISSN 0021-9991, 06/2016, Volume 315, pp. 84 - 97

The numerical approximation of the distributed order time fractional reaction–diffusion equation on a semi-infinite spatial domain is discussed in this paper....

Spectral method | Fractional diffusion | Distributed order differential equation | Error estimate | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | CALCULUS | GALERKIN METHOD | SUBDIFFUSION EQUATION | ARTIFICIAL BOUNDARY-CONDITIONS | PHYSICS, MATHEMATICAL | WAVE-EQUATIONS | SCHEMES | Analysis | Differential equations | Approximation | Mathematical analysis | Exact solutions | Mathematical models | Polynomials | Spectra | Reaction-diffusion equations | Finite difference method

Spectral method | Fractional diffusion | Distributed order differential equation | Error estimate | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | CALCULUS | GALERKIN METHOD | SUBDIFFUSION EQUATION | ARTIFICIAL BOUNDARY-CONDITIONS | PHYSICS, MATHEMATICAL | WAVE-EQUATIONS | SCHEMES | Analysis | Differential equations | Approximation | Mathematical analysis | Exact solutions | Mathematical models | Polynomials | Spectra | Reaction-diffusion equations | Finite difference method

Journal Article

Journal of Computational Physics, ISSN 0021-9991, 11/2018, Volume 372, pp. 616 - 639

We present a novel hyperviscosity formulation for stabilizing RBF-FD discretizations of the advection–diffusion equation. The amount of hyperviscosity is...

Radial basis function | Hyperviscosity | High-order method | Advection–diffusion | Meshfree | NONLINEAR CONSERVATION-LAWS | APPROXIMATIONS | SPHERE | ALGORITHM | INTERPOLANTS | PHYSICS, MATHEMATICAL | POLYNOMIALS | SPECTRAL VISCOSITY METHOD | TRANSPORT | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | Advection-diffusion | STABLE COMPUTATIONS | SURFACES | advection-diffusion | meshfree | high-order method | hyperviscosity

Radial basis function | Hyperviscosity | High-order method | Advection–diffusion | Meshfree | NONLINEAR CONSERVATION-LAWS | APPROXIMATIONS | SPHERE | ALGORITHM | INTERPOLANTS | PHYSICS, MATHEMATICAL | POLYNOMIALS | SPECTRAL VISCOSITY METHOD | TRANSPORT | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | Advection-diffusion | STABLE COMPUTATIONS | SURFACES | advection-diffusion | meshfree | high-order method | hyperviscosity

Journal Article

Scandinavian Journal of Statistics, ISSN 0303-6898, 9/2008, Volume 35, Issue 3, pp. 438 - 465

The Pearson diffusions form a flexible class of diffusions defined by having linear drift and quadratic squared diffusion coefficient. It is demonstrated that...

Ergodic theory | Maximum likelihood estimation | Eigenfunctions | Polynomials | Mathematical moments | Stochastic models | Ornstein Uhlenbeck process | Estimators | Martingales | Estimation methods | spectral methods | prediction‐based estimating function | mixing | eigenfunction | likelihood inference | optimal estimating function | ergodic diffusion | Pearson system | martingale estimating function | stochastic differential equation | quasi‐likelihood | integrated diffusion | stochastic volatility | Martingale estimating function | Prediction-based estimating function | Integrated diffusion | Mixing | Quasi-likelihood | Eigenfunction | Optimal estimating function | Spectral methods | Ergodic diffusion | Likelihood inference | EXCHANGE-RATES | prediction-based estimating function | EQUATIONS | STATISTICS & PROBABILITY | quasi-likelihood | MODELS | FINANCE | DISCRETE | CONTINUOUS-TIME | VOLATILITY | LIKELIHOOD | MOMENTS

Ergodic theory | Maximum likelihood estimation | Eigenfunctions | Polynomials | Mathematical moments | Stochastic models | Ornstein Uhlenbeck process | Estimators | Martingales | Estimation methods | spectral methods | prediction‐based estimating function | mixing | eigenfunction | likelihood inference | optimal estimating function | ergodic diffusion | Pearson system | martingale estimating function | stochastic differential equation | quasi‐likelihood | integrated diffusion | stochastic volatility | Martingale estimating function | Prediction-based estimating function | Integrated diffusion | Mixing | Quasi-likelihood | Eigenfunction | Optimal estimating function | Spectral methods | Ergodic diffusion | Likelihood inference | EXCHANGE-RATES | prediction-based estimating function | EQUATIONS | STATISTICS & PROBABILITY | quasi-likelihood | MODELS | FINANCE | DISCRETE | CONTINUOUS-TIME | VOLATILITY | LIKELIHOOD | MOMENTS

Journal Article

Mathematics of Computation of the American Mathematical Society, ISSN 0025-5718, 09/2018, Volume 87, Issue 313, pp. 2273 - 2294

In this article we investigate the solution of the steady-state fractional diffusion equation on a bounded domain in \mathbb{R}^{1}. The diffusion operator...

Jacobi polynomials | Spectral method | Fractional diffusion equation | SPECTRAL METHOD | MATHEMATICS, APPLIED | FINITE-DIFFERENCE METHOD | FORMULATION | APPROXIMATIONS | GALERKIN METHOD

Jacobi polynomials | Spectral method | Fractional diffusion equation | SPECTRAL METHOD | MATHEMATICS, APPLIED | FINITE-DIFFERENCE METHOD | FORMULATION | APPROXIMATIONS | GALERKIN METHOD

Journal Article

Journal of Magnetic Resonance Imaging, ISSN 1053-1807, 09/2019, Volume 50, Issue 3, pp. 899 - 909

Background The fetal brain developmental changes of water diffusivity and perfusion has not been extensively explored. Purpose/Hypothesis To evaluate the fetal...

brain development | white matter | gestational age | intravoxel incoherent motion | fetus | IN-UTERO | MRI | MATURATION | COEFFICIENT | PERFUSION | FETUSES | CELL | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Cerebellum | Neuroimaging | Brain | Basal ganglia | Pons | Model testing | Population studies | Gestational age | Regression models | Developmental stages | Thalamus | Polynomials | Mathematical models | Diffusion coefficient | Diffusion | Medical imaging | Statistical analysis | Parameters | Fetuses | Nonlinear analysis | Regression analysis | Statistical tests | Substantia alba | Variance analysis | Diffusivity | Ganglia | Pregnancy | Field strength | Population (statistical) | Magnetic resonance imaging | Perfusion

brain development | white matter | gestational age | intravoxel incoherent motion | fetus | IN-UTERO | MRI | MATURATION | COEFFICIENT | PERFUSION | FETUSES | CELL | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | Cerebellum | Neuroimaging | Brain | Basal ganglia | Pons | Model testing | Population studies | Gestational age | Regression models | Developmental stages | Thalamus | Polynomials | Mathematical models | Diffusion coefficient | Diffusion | Medical imaging | Statistical analysis | Parameters | Fetuses | Nonlinear analysis | Regression analysis | Statistical tests | Substantia alba | Variance analysis | Diffusivity | Ganglia | Pregnancy | Field strength | Population (statistical) | Magnetic resonance imaging | Perfusion

Journal Article

Journal of Optimization Theory and Applications, ISSN 0022-3239, 11/2019, Volume 183, Issue 2, pp. 688 - 704

In this paper, we consider an inverse reaction–diffusion–convection problem in which one of the boundary conditions is unknown. A sixth-kind Chebyshev...

35R30 | Mollification | Mathematics | Theory of Computation | 35K15 | 41A50 | Reaction–diffusion–convection equation | Optimization | Inverse problem | Calculus of Variations and Optimal Control; Optimization | 65M70 | Operations Research/Decision Theory | Error estimate | Applications of Mathematics | Engineering, general | Collocation method | Sixth-kind Chebyshev polynomials | MATHEMATICS, APPLIED | NUMERICAL-SOLUTION | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | Reaction-diffusion-convection equation | Algorithms | Inverse problems | Chebyshev approximation | Collocation methods | Boundary conditions | Regularization methods | Ill-posed problems (mathematics) | Regularization | Convection

35R30 | Mollification | Mathematics | Theory of Computation | 35K15 | 41A50 | Reaction–diffusion–convection equation | Optimization | Inverse problem | Calculus of Variations and Optimal Control; Optimization | 65M70 | Operations Research/Decision Theory | Error estimate | Applications of Mathematics | Engineering, general | Collocation method | Sixth-kind Chebyshev polynomials | MATHEMATICS, APPLIED | NUMERICAL-SOLUTION | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | Reaction-diffusion-convection equation | Algorithms | Inverse problems | Chebyshev approximation | Collocation methods | Boundary conditions | Regularization methods | Ill-posed problems (mathematics) | Regularization | Convection

Journal Article

Computer Methods in Applied Mechanics and Engineering, ISSN 0045-7825, 04/2017, Volume 317, pp. 723 - 745

We study multigrid (MG) methods for the solution of systems of linear algebraic equations obtained from a stable discretization of convection–diffusion...

Variable-step preconditioning | Convection–diffusion equation | Polynomial smoother | Exponential fitting scheme | Faber polynomials | AMLI-cycle multigrid | CONVERGENCE ANALYSIS | NONSYMMETRIC SYSTEMS | EQUATIONS | MULTILEVEL PRECONDITIONING METHODS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MECHANICS | ENGINEERING, MULTIDISCIPLINARY | TCHEBYCHEV ITERATION | Convection-diffusion equation | Analysis | Methods | Algorithms

Variable-step preconditioning | Convection–diffusion equation | Polynomial smoother | Exponential fitting scheme | Faber polynomials | AMLI-cycle multigrid | CONVERGENCE ANALYSIS | NONSYMMETRIC SYSTEMS | EQUATIONS | MULTILEVEL PRECONDITIONING METHODS | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MECHANICS | ENGINEERING, MULTIDISCIPLINARY | TCHEBYCHEV ITERATION | Convection-diffusion equation | Analysis | Methods | Algorithms

Journal Article

Communications in Nonlinear Science and Numerical Simulation, ISSN 1007-5704, 2011, Volume 16, Issue 6, pp. 2535 - 2542

Fractional differential equations have recently been applied in various area of engineering, science, finance, applied mathematics, bio-engineering and others....

Fractional diffusion equation | Caputo derivative | Chebyshev polynomials | Finite difference method | MATHEMATICS, APPLIED | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MECHANICS | PHYSICS, FLUIDS & PLASMAS | PHYSICS, MATHEMATICAL | FLOW | FINITE-DIFFERENCE APPROXIMATIONS | Approximation | Computer simulation | Mathematical analysis | Differential equations | Chebyshev approximation | Mathematical models | Derivatives | Diffusion

Fractional diffusion equation | Caputo derivative | Chebyshev polynomials | Finite difference method | MATHEMATICS, APPLIED | MATHEMATICS, INTERDISCIPLINARY APPLICATIONS | MECHANICS | PHYSICS, FLUIDS & PLASMAS | PHYSICS, MATHEMATICAL | FLOW | FINITE-DIFFERENCE APPROXIMATIONS | Approximation | Computer simulation | Mathematical analysis | Differential equations | Chebyshev approximation | Mathematical models | Derivatives | Diffusion

Journal Article