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Номер журнала: 2019.3

Заголовок статьи: Management of an application software package for solving problems of mathematical physics via semantic network

Резюме

The numerical solution of mathematical physics problems using a computer can be divided into several stages: geometric model of the computational domain construction, grid model construction, function approximation, derivatives and integrals, as well as equations solving. There are many grid generators and algorithms for constructing two-dimensional and three-dimensional grids, programs for systems of equations solving, approximators and geometric modeling tools. While creating an application software package for solving problems of mathematical physics based on the concept of basic modeling system, each stage of the problem solving representing as a separate module. Each module, in turn, can be a set of algorithms and subroutines. Such a software package ensures the integrity of the solution to a computational problem thanks to a wide range of tools for going through any of the computational stages and allows you to vary the input parameters and choose the most suitable algorithms at different stages. Moreover, such a system allows the decomposition of the initial computational domain into subdomains when constructing the geometry and the generation of a quasistructured grid model. However, when new algorithms and programs are included into the computing complex, the complexity of its use increases inevitably. Thus, there is a need for the design of a supersystem allowing us to determine the best set of algorithms for subtasks solving at each stage in terms of some quality criteria defined in advance. The purpose of this work is to develop and describe such a model for managing this computing complex using a knowledge base presented in the form of semantic network.

Авторы

L. A. Golubeva, V. S. Gorshunov, V. P. Il’in

Ключевые слова

mathematical physics, geometric data structure, grid data structure, functional data structure, BMS, semantic networks, knowledge bases

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