The focus of this paper is on the numerical solution of large sparse non-linear systems of algebraic 9 equations on parallel computers. Such non-linear systems often arise from the discretization of non-linear partial di erential equations, such as the Navier–Stokes equations for uid ows, using nite element 11 or nite di erence methods. A traditional inexact Newton method, applied directly to t...

We are interested in modelling Darwinian evolution, resulting from the interplay of phenotypic variation and natural selection through ecological interactions. Our models are rooted in the microscopic, stochastic description of a population of discrete individuals characterized by one or several adaptive traits. The population is modelled as a stochastic point process whose generator captures t...

In this paper we review and further develop a class of strong-stability preserving (SSP) high-order time discretizations for semi-discrete method-of-lines approximations of partial di erential equations. Termed TVD (total variation diminishing) time discretizations before, this class of high-order time discretization methods preserves the strong-stability properties of rst-order Euler time step...

. We consider a multivariate continuous time process, generated by a system of linear stochastic di¤erential equations, driven by white noise and involving coe¢ cients that possibly vary over time. The process is observable only at discrete, but not necessarily equally-spaced, time points (though equal spacing signi…cantly simpli…es matters). Such settings represent partial extensions of ones s...

An approach for transforming systems of partial di erential equations in order to obtain new formulations which are more accessible to numerical solution is studied. An algorithm is developed for generating such transformations automatically, using symbolic computations employing Gr obner bases. The algorithm is implemented using freely available software. This approach, along with planned dev...

Einstein's eld equations for spatially self-similar locally rotationally symmetric perfect uid models are investigated. The eld equations are rewritten as a rst order system of autonomous ordinary di erential equations. Dimensionless variables are chosen in such a way that the number of equations in the coupled system of di erential equations is reduced as far as possible. The system is subsequ...

a novel and eective method based on haar wavelets and block pulse functions(bpfs) is proposed to solve nonlinear fredholm integro-dierential equations of fractional order.the operational matrix of haar wavelets via bpfs is derived and together with haar waveletoperational matrix of fractional integration are used to transform the mentioned equation to asystem of algebraic equations. our new m...

in this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. utilizing the generalized characterization theorem. then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. here neural network is considered as a part of large eld called n...

Domain decomposition methods for the solution of semi-linear selfadjoint and non-selfadjoint singularly perturbed di erential equations are considered. An iterative domain decomposition algorithm suitable for parallelization is described and convergence of the algorithm is established. The implementation on a shared memory multiprocessor is described and numerical examples are presented to demo...

We classify all partial di¤erential equations with polynomial coe¢ cients in x and y of the form A(x)uxx + 2B(x; y)uxy + C(y)uyy +D(x)ux + E(y)uy = nu; which has weak orthogonal polynomials as solutions and show that partial derivatives of all order are orthogonal. Also, we construct orthogonal polynomials in d-variables satisfying second order partial di¤erential equations in d-variables.