L.Brugnano and J.R.Cash
For a number of years this special issue of JNAIAM has been devoted to the proceedings of
the ICNAAM Conference series. In particular the seventh conference, held in Rethymno, Crete
(GR), from 18th to 22th September 2009, celebrates the 60th birthday of Professor Ernst Hairer.
As is well known, Ernst is one of the leading experts in the numerical solution of ODEs. He has
contributed substantially to the field, both in the theoretical analysis of numerical methods, and
from the point of view of software development. He is coauthor of a number of monographs on this
topic, as well as of some of the most reliable codes for stiff ODEs, based on Radau IIA formulae.
One of the fields where he has been very involved in the last few years is that of geometric numerical
integration, where he is coauthor, with Christian Lubich and Gerard Wanner, of one of the most
comprehensive monographs on the subject.
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T.E. Simos2
Department of Mathematics, College of Sciences, King Saud University,
P. O. Box 2455, Riyadh 11451, Saudi Arabia and
Department of Computer Science and Technology,
Faculty of Sciences and Technology, University of Peloponnese,
GR-221 00 Tripolis, Greece
This is to remind you that the affiliation of the Journal of Numerical Analysis, Industrial and
Applied Mathematics is:
Journal of Numerical Analysis, Industrial and Applied Mathematics
(JNAIAM)
This is very important
European Society of Computational Methods in Sciences and EngineeringEuropean Society of Computational Methods in Sciences and Engineering (ESCMSE) is a non-profit organization. The aims and scopes of ESCMSE is the construction, the development and the analysis of computational, numerical and mathematical methods and the application of the developed methods in sciences and engineering. The activities of ESCMSE are on the subject of computational, numerical and mathematical methods in sciences and engineering. We invite you to become part of this exciting new international project and participate in the promotion and exchange of ideas in your field.
JNAIAM
Subject: Mathematics
Date: 01/08/2006
Published by: Promacon
Frequency: 4
ISSN: 1790-8140 ...
ISSN Electronic: 1790-8159
Copyrights: ESCMSE
Mathematical Reviews (MathSciNet) Zentralblatt MATH Database
European Societies of Computational Methods in Science and Engineering
ESCMSE
Editorial Board
Editor-in-Chief and Founder : T.E. Simos, Greece, King Saud University, Ural Federal University, TEI of Sterea Hellas, Democritus University of Thrace
Assistant Editor-in-Chief : G. Psihoyios, UK
Editorial Assistant : E. Ralli-Simou
Editors :
P. E. Bjørstad, Norway
S. C. Brenner, USA
J. Cash, UK
Mario Collotta, Italy
R. Cools, Belgium
A. Cuyt, Belgium
R. W. Freund, USA
I. Gladwell, USA
A. Klar, Germany
G. Vanden Berghe, Belgium
G. Alistair Watson, UK
D.P. Laurie, South Africa
Martin Berzins
Luigi Brugnano
J.C. Butcher
Daniel W. Lozier
Brynjulf Owren
International Conference of Computational Methods in Science and Enginnering
ICCMSE 2008
International Conference of Numerical Analysis and Applied Mathematics
ICNAAM 2008
International e-Conference on Computer Science
IeCCS 2008
Monday, September 3. 2018
R. Boonklurb1, A. Duangpan2 and T. Treeyaprasert3
1,2Department of Mathematics and Computer Science, Faculty of Science,
Chulalongkorn University, Bangkok 10330, Thailand
3Department of Mathematics and Statistics, Thammasat University,
Rangsit Center, Pathumthani 12120, Thailand
Received 10 January 2018; accepted in revised form 27 August 2018
Abstract: We propose a modified finite integration method (FIM) by using the Chebyshev polynomial, to construct the first order integral matrix for solving linear differential equations in one and two dimensions. The grid points for the computation are generated by the zeros of the Chebyshev polynomial of a certain degree. We implement our method with several examples arose from real-world applications. In comparison with the finite difference method and the traditional FIMs (trapezoidal and Simpson's rules), numerical computations show that our modified FIM using Chebyshev nodes require the less computational cost to achieve significant improvement in accuracy.
c 2018 European Society of Computational Methods in Sciences and Engineering
Keywords: Finite integration method, Linear di fferential equations, Chebyshev polynomial.
Mathematics Subject Classi cation: 65L05, 65L10, 65N30
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Monday, July 16. 2018
V.V. Kozhevnikov1, V.V. Prikhodko1, V.V. Svetukhin1, A.V. Zhukov1, A.N. Fomin1, M.Yu. Leontyev1, D.Ya. Vostretsov1, A.A. Sobolev1, V.I. Skrebtsov1, V.E. Kiryukhin1, V.V. Levschanov1, D.S. Lavygin1, E.M. Chavkin1, E.R. Mingachev1, R.G. Bildanov1, S.V. Pavlov2, V.N. Kovalnogov1
1 S.P. Kapitsa Research Institute of Technology (Technological Research Institute) of Ulyanovsk State University, Ulyanovsk, Russia
2 Sosny Research and Development Company, Dimitrovgrad, Ulyanovsk region, Russia
Received 29 June, 2018; accepted in revised form 17 July, 2018
Abstract
Principal directions of developing the methods for designing intelligent systems of robot control assume technologies based on the use of artificial neural networks. The neural networks, where the model of a neuron was developed as the simplest processor element, performing the computation of the transfer function of a scalar product of an input data vector and a weight vector, can give interesting results regarding generation of dependencies and forecasting. However, their obvious drawback is the lack of an explicit algorithm of action. Memorization of information in the learning process occurs implicitly as a result of selection of the weight coefficients of the neural network, therefore the problem of cognition (the formation of new knowledge) on the basis of those obtained earlier in the learning process seems difficult to resolve. A positive solution to this problem will open the way to the creation of the full-fledged artificial mind. From this point of view the promising area is where the mathematical model of the neural networks is built on the basis of mathematical logic. The intelligent control system in this case is a software and hardware complex, where the mathematical model of the neural network identifies the control system as an intellectual one.
c 2018 European Society of Computational Methods in Sciences and Engineering
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Friday, April 6. 2018
R. Arcucciab1, L.Carracciuoloc and R.Toumid
a: University of Naples Federico II, Naples, Italy
b: Euro Mediterranean Center on Climate Change, Italy
c: National Research Council, Naples, Italy
d: Imperial College London, London, United Kingdom
Received 1 February, 2017; accepted in revised form 03 April, 2018
Abstract: Data Assimilation (DA) is an uncertainty quanti cation technique used to
incorporate observed data into a prediction model in order to improve numerical forecasted
results. As a crucial point into DA models is the ill conditioning of the covariance matrices
involved, it is mandatory to introduce, in a DA software, preconditioning methods. Here
we present rst results obtained introducing two di erent preconditioning methods in
a DA software we are developing (we named S3DVAR) which implements a Scalable
Three Dimensional Variational Data Assimilation model for assimilating sea surface
temperature (SST) values collected into the Caspian Sea by using the Regional Ocean
Modeling System (ROMS) with observations provided by the Group of High resolution
sea surface temperature (GHRSST). We present the algorithmic strategies we employ
and the numerical issues on data collected in two of the months which present the most
signi cant variability in water temperature: August and March.
c 2018 European Society of Computational Methods in Sciences and Engineering
Keywords: Data Assimilation, oceanographic data, Sea Surface Temperature, Caspian sea,
ROMS
Mathematics Subject Classi cation: 65Y05, 65J22, 68W10, 68U20
PACS: 02.70.-c
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Monday, March 26. 2018
O. Ozturk
Department of Mathematics,
Faculty of Arts and Sciences,
Bitlis Eren University,
13000 Bitlis, Turkey
Received 10 October, 2016; accepted in revised form 22 March, 2018
Abstract: Fractional calculus and its generalizations are used for the solutions of some classes of differential
equations and fractional differential equations. In this paper, our aim is to solve the radial Schrödinger
equation given by the Makarov potential by the help of fractional calculus theorems. The related equation
was solved by applying a fractional calculus theorem that gives fractional solutions of the second order
differential equations with singular points. In the last section, we also introduced hypergeometric form of
this solution.
© European Society of Computational Methods in Sciences and Engineering
Keywords: Fractional calculus, Generalized Leibniz rule, Radial Schrödinger equation, Makarov potential
Mathematics Subject Classification: 26A33, 34A08
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Tuesday, February 7. 2017
William F. Mitchell
Applied and Computational Mathematics Division National Institute of Standards and Technology Gaithersburg, MD, USA 20899-8910
Abstract: One aspect of adaptive mesh refinement in the finite element method for solving partial differential equations is the method by which elements are refined. In the early 1980’s the dominant method for refining triangles was the red-green algorithm of Bankand Sherman. The red refinements are the desired refinements, but will result in an incompatible mesh when used alone. The green refinements are used to recover compatibility for stability of the finite element discretization, and are removed before the next adaptive step. Prof. Bob Skeel raised the question as to whether it is possible to perform adaptive refinement of triangles without this complicated patching/unpatching process. As a result, a new triangle refinement method, called newest vertex bisection, was devised as an alternative to red-green refinement in the mid 1980’s. The new approach is simpler and maintains compatibility of the mesh at all times, avoiding the patching/unpatching of the green refinement. In this historical paper we review the development of the newest vertex bisection method for adaptive refinement, and subsequent extensions of the method.
Keywords: finite elements; adaptive mesh refinement; newest vertex bisection
Mathematics Subject Classification: 65-03, 65N30, 65N50
PACS: 02.60.Lj, 02.70.Dh
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Tuesday, February 7. 2017
Gaussian Scale Mixtures Miguel Martins Felgueiras 1 *, João Paulo Martins**, Rui Filipe Santos**
* CEAUL Lisbon and ESTG, CIGS, Polytechnic Institute of Leiria, Portugal
** CEAUL Lisbon and ESTG, Polytechnic Institute of Leiria, Portugal
Abstract: In this paper we present a parsimonious approximation of a Gaussian mixture when its components share a common mean value, i.e. a scale mixture. We show that a shifted and scaled Student’s t-distribution can be approximated to this type of mixture, and use the result to develop a hypothesis test for the equality of the components mean value. A simulation study to check the quality of the approximation is also provided, together with an application to logarithmic daily returns.
Keywords: Gaussian scale mixture, Student’s t-distribution, log-returns, simulation.
Mathematics Subject Classification: 60E05; 62P20.
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