The transport and continuum equations exhibit a number of conservation laws. For example, scalar multiplication is conserved by the transport equation, while positivity of probabilities is conserved by the continuum equation. Certain discretization techniques, such as particle based methods, conserve these properties, but converge slower than spectral discretization methods on smooth data. Stan...

We consider an eddy current problem in time-domain relying on impedance boundary conditions on the surface of the conductor(s). We pursue its full discretization comprising (i) a finite element Galerkin discretization by means of lowest order edge elements in space, and (ii) temporal discretization based on Runge-Kutta convolution quadrature (CQ) for the resulting Volterra integral equation in ...

Truncation-error analysis is a reliable tool in predicting convergence rates of discretization errors on regular smooth grids. However, it is often misleading in application to finitevolume discretization schemes on irregular (e.g., unstructured) grids. Convergence of truncation errors severely degrades on general irregular grids; a design-order convergence can be achieved only on grids with a ...

We use the idea of nonstandard finite difference methods to derive the discrete variational integrators for multisymplectic PDEs. We obtain a nonstandard finite difference variational integrator for linear wave equation with a triangle discretization and two nonstandard finite difference variational integrators for the nonlinear Klein-Gordon equation with a triangle discretization and a square ...

Discrete differential geometry aims to develop discrete equivalents of the geometric notions and methods of classical differential geometry. This survey contains a discussion of the following two fundamental discretization principles: the transformation group principle (smooth geometric objects and their discretizations are invariant with respect to the same transformation group) and the consis...

In this paper we address the problem of discretization in the context of learning Bayesian networks (BNs) from data con­ taining both continuous and discrete vari­ ables. We describe a new technique for multivariate discretization, whereby each continuous variable is discretized while tak­ ing into account its interaction with the other variables. The technique is based on the use of a Bayesian...

A concept of fuzzy discretization of feature space for a rough set theoretic classifier is explained. Fuzzy discretization is characterised by membership value, group number and affinity corresponding to an attribute value, unlike crisp discretization which is characterised only by the group number. The merit of this approach over both crisp discretization in terms of classification accuracy, i...

Connections of KP, qKP, and Moyal type dKP constructions are developed. Some expansion of the Moyal KP constructions of Kemmoku-Saito is given with clarification of the role of spectral variables as a phase space.

This is an overview of a few possibilities that are open by model theory in applied mathematics. The most attention is paid to the present state and frontiers of the Cauchy method of majorants, approximation of operator equations with finite-dimensional analogs, and the Lagrange multiplier principle in multiobjective decision making. DOI: 10.1134/S1990478909010116 The union of functional analys...