5,683 research outputs found
A collocation method for solving some integral equations in distributions
A collocation method is presented for numerical solution of a typical
integral equation Rh :=\int_D R(x, y)h(y)dy = f(x), x {\epsilon} D of the class
R, whose kernels are of positive rational functions of arbitrary selfadjoint
elliptic operators defined in the whole space R^r, and D \subset R^r is a
bounded domain. Several numerical examples are given to demonstrate the
efficiency and stability of the proposed method.Comment: 26 pages, 4 figure
Sensitivity And Out-Of-Sample Error in Continuous Time Data Assimilation
Data assimilation refers to the problem of finding trajectories of a
prescribed dynamical model in such a way that the output of the model (usually
some function of the model states) follows a given time series of observations.
Typically though, these two requirements cannot both be met at the same
time--tracking the observations is not possible without the trajectory
deviating from the proposed model equations, while adherence to the model
requires deviations from the observations. Thus, data assimilation faces a
trade-off. In this contribution, the sensitivity of the data assimilation with
respect to perturbations in the observations is identified as the parameter
which controls the trade-off. A relation between the sensitivity and the
out-of-sample error is established which allows to calculate the latter under
operational conditions. A minimum out-of-sample error is proposed as a
criterion to set an appropriate sensitivity and to settle the discussed
trade-off. Two approaches to data assimilation are considered, namely
variational data assimilation and Newtonian nudging, aka synchronisation.
Numerical examples demonstrate the feasibility of the approach.Comment: submitted to Quarterly Journal of the Royal Meteorological Societ
An advanced meshless method for time fractional diffusion equation
Recently, because of the new developments in sustainable engineering and renewable energy, which are usually governed by a series of fractional partial differential equations (FPDEs), the numerical modelling and simulation for fractional calculus are attracting more and more attention from researchers. The current dominant numerical method for modeling FPDE is Finite Difference Method (FDM), which is based on a pre-defined grid leading to inherited issues or shortcomings including difficulty in simulation of problems with the complex problem domain and in using irregularly distributed nodes. Because of its distinguished advantages, the meshless method has good potential in simulation of FPDEs. This paper aims to develop an implicit meshless collocation technique for FPDE. The discrete system of FPDEs is obtained by using the meshless shape functions and the meshless collocation formulation. The stability and convergence of this meshless approach are investigated theoretically and numerically. The numerical examples with regular and irregular nodal distributions are used to validate and investigate accuracy and efficiency of the newly developed meshless formulation. It is concluded that the present meshless formulation is very effective for the modeling and simulation of fractional partial differential equations
Differential quadrature method for space-fractional diffusion equations on 2D irregular domains
In mathematical physics, the space-fractional diffusion equations are of
particular interest in the studies of physical phenomena modelled by L\'{e}vy
processes, which are sometimes called super-diffusion equations. In this
article, we develop the differential quadrature (DQ) methods for solving the 2D
space-fractional diffusion equations on irregular domains. The methods in
presence reduce the original equation into a set of ordinary differential
equations (ODEs) by introducing valid DQ formulations to fractional directional
derivatives based on the functional values at scattered nodal points on problem
domain. The required weighted coefficients are calculated by using radial basis
functions (RBFs) as trial functions, and the resultant ODEs are discretized by
the Crank-Nicolson scheme. The main advantages of our methods lie in their
flexibility and applicability to arbitrary domains. A series of illustrated
examples are finally provided to support these points.Comment: 25 pages, 25 figures, 7 table
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