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
Friday, December 7. 2018
THE CONCEPT OF DESIGNING AN INTELLECTUAL ROBOT CONTROL SYSTEM
BASED ON THE MATHEMATICAL MODEL OF COGNITIVE DIGITAL AUTOMATA
V.V.Kozhevnikov1, B.M.Kostishko1, M.Yu.Leontev1, E.R.Mingachev1, S.V.Pavlov2, V.V.Prikhodko1
1S.P. Kapitsa Technological Research Institute of Ulyanovsk State University, Ulyanovsk, Russia
2Sosny Research and Development Company, Dimitrovgrad, Ulyanovsk region, Russia
Received: 10 November 2018; accepted in revised form: 04 December 2018
Abstract. An intellectual control system (ICS) to control robots can be built (designed) on the basis of the mathematical model of cognitive digital automata (CDA). The intellectual control system in this case is a software and hardware complex, where the mathematical model of the CDA determines the control system as an intellectual one. The cognitive ability of the mathematical model is determined by the possibility of forming new knowledge based on the knowledge gained at the training stage. The creativity of a mathematical model is determined by the ability to construct sequences (logical chains) of generating new knowledge.
A specific feature of the mathematical model of the CDA consists in the fact that the description of the neural network (NN) structure serves as the initial structural scheme of automata, and the logical function "NOT-AND-OR" is used as the neuron model. The mathematical apparatus of Petri nets (PN) is proposed as a tool to construct the mathematical model of the CDA.
The structure, composition and algorithm of functioning of the robot’s intellectual control system based on the mathematical model of the CDA is discussed in the paper. In accordance with the algorithm, the ICS operates in three modes: training, manual and automatic control. Training a mathematical model of the CDA can be performed both in the manual mode and in the automatic control mode. The possibility of learning in the automatic control mode, in its turn, provides the possibility of regenerating knowledge and, accordingly, the possibility of cognitive control.
Keywords: intellectual control system, robots, cognitive automata, artificial intelligence, neural networks, machine learning, cognition, thinking, Petri nets, equation of states, mathematical modeling, synthesis, generation, analysis, logic.
c 2018 European Society of Computational
Methods in Sciences and Engineering
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Monday, November 12. 2018
NICOLETA E. TARFULEA
DEPARTMENT OF MATHEMATICS, PURDUE UNIVERSITY NORTHWEST, USA
Abstract. HIV can infect cells via cell-to-cell and virus-to-cell transmissions. These two types of transmission may occur in a combined way and enable viral spread. In this paper, we investigate analytically and numerically the influence of these two transmission modes, as well as the viral loss due to infection of T-cells. We introduce, analyze, and compare three mathematical models and show that viral loss due to infection of cells has little effect on the dynamics of HIV. Moreover, we show that additional conditions for the steady state stability are needed when virus-to-cell-transmission is included and a critical value for this parameter is provided. Numerical simulations illustrate the theoretical results and further investigate the differences between these systems.
c 2018 European Society of Computational
Methods in Sciences and Engineering
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Monday, October 15. 2018
N. Chaabane1,a, B. Rivierea, Mikhail Sekachevb and Henri Calandrab
aCAAM department, Rice University
bTotal E&P Research & Technology USA, LLC
1E-mail: nc33@rice.edu
Abstract: In [7], a sequential approach was
introduced to solve the Biot system where the pressure and displacement
variables are decoupled. A stabilization term was added and the discontinuous
Galerkin method was used to discretize the equations in space and the backward
Euler method was used to discretize the equations in time. The convergence of
the method was established both theoretically and numerically. In this work, we
run several numerical experiments to further validate this approach. Cases with
more complex boundary conditions and realistic input parameters are solved. We
also carry out a strong scalability analysis to show the efficiency of this
method on supercomputers.
c 2018 European Society of Computational
Methods in Sciences and Engineering
Keywords: Poroelasticity; Biot system;
Discontinuous Galerkin; Barry-Mercer; sequential method; parallel
implementation
Mathematics Subject Classiffication: 65M60
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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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