Treffer: A scientific computing environment for differential field simulation

Title:
A scientific computing environment for differential field simulation
Source:
Mathematics and computers in simulation. 63(2):79-91
Publisher Information:
Amsterdam: Elsevier, 2003.
Publication Year:
2003
Physical Description:
print, 16 ref
Original Material:
INIST-CNRS
Subject Terms:
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Istituto per le Applicazioni del Calcolo, Viale del Policlinico 137, 00161 Rome, Italy
ISSN:
0378-4754
Rights:
Copyright 2003 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Mathematics
Accession Number:
edscal.14859330
Database:
PASCAL Archive

Weitere Informationen

This paper deals with the development of a scientific computing environment for differential field simulation. We mean a modelling and simulation environment based on partial differential equations and their numerical solution as powerful and widely used techniques for mathematical and computational investigation of application problems. We have been developing grid generation algorithms, numerical solvers of PDE systems, along with advanced visualization techniques, to numerically compute and evaluate field variables by exploiting user-friendly interaction. In this paper, we model the complete cycle of the visual computational simulation as reference framework and we illustrate advances in the environment development. We describe a few computational components by focusing on two fundamental substeps often concurring to simulation processes, the image segmentation and grid generation. We introduce differential equation systems, developed combination of computational methods and recent algorithmic advances. A few application results are detailed, and shown by figures, for segmentation test problems.