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510 Labidi et al.pk+k+ppkpFigure 2. After performing (m − k) implicitly shifted QR steps on H m, the middle set ofpictures illustrates V mQH + m + f me T mQ. The last (m − k) columns of f me T mQ are non zerobecause of the QR iteration.k+kkFigure 3. An implicitly restarted Arnoldi factorisation of length k results after discardingthe last (m − k) columns of Figure 2.3. Application of IRAM for computing guided modes in anoptical fibreWe are interested in computing the propagation constants and their associated electromagneticfields of guided modes that propagate, under the conditions of weak guidance, inan optical fibre with an arbitrary shape and a general refractive-index profile. An efficientmethod coupling a local boundary condition of Robin type to a finite element techniqueis proposed in [2]. This consists on solving the following non-linear eigenvalue problem.⎧⎪⎨(P Σ )⎪⎩F ind λ Σ ∈]0, V 2 [ and u Σ ∈ H 1 (Ω Σ ); u Σ ≠ 0 such that− △ u Σ + q(x)u Σ = −λ Σ u Σ dans Ω Σ∂u Σ∂ν + (√ λ Σ + K 2 )u Σ = 0 sur ΣΩ Σ is a bounded domain of R 2 , with Σ = ∂Ω Σ . We place Σ in such way that Ω Σ containsthe core of considered fibre, and hence the support of the function q ∈ L ∞ (R 2 ).Σ canhave an arbitrary shaped, but must be convex (cf. [2]). K denotes the curvature of theconsidered artificial boundary Σ. −→ ν denotes the outward directed unitary normal vector.(λ Σ , u Σ ) approximates a guided mode that propagates in the considered optical fibre.TAMTAM –Tunis– 2005
IRAM method 511The application of the finite element method to the variational problem corresponding to(P Σ ) leads to the following generalized eigenvalue problem :{F ind λh(PΣ)h Σ ∈]0, V 2 [ and z ∈ R 2N ; z ≠ 0 such that√Ãz = λ h ˜B Σz (1)where Ã and ˜B are large-scale, sparse matrices defined by( ) ( −C −BB 0Ã =et ˜B =A 00 BThe matrix ˜B is symmetric and positive definite. Ã is such that its blocks A, B, and C areclassical matrices of mass or/and rigidity type.Suppose that we wish to compute k generalized eigenvalues of the pencil (Ã, ˜B) closestto a given shift σ, with k < m
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PréfaceOrganiser la seconde éditi
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Comité d’organisationMohamed Abd
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SOMMAIRE / CONTENTSI Conférences I
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Equilibre dans un système dynamiqu
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Arlequin method: Practical impacts
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Application de la méthode de Gauss
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Conférences InvitéesInvited Prese
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4 Amara et al.1. IntroductionWe are
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6 Amara et al.2.2. The nonlinear Na
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8 Amara et al.4. Numerical resultsW
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10 Auger et al.1. Introduction :Dan
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12 Auger et al.La condition de stab
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14 Auger et al.3.1. Taux de migrati
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16 Auger et al.L’équilibre rapid
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18 Auger et al.[7] BRAVO DE LA PARR
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Quelques aspects mathématiques etn
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22 El Alaoui Talibi M.- Modèle d
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24 El Alaoui Talibi M.[3] M. Chamba
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26 Habbal1. IntroductionAngiogenesi
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28 HabbalThe pressure drop denoted
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30 Habbaltheorem 1 There exists a N
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32 HabbalFigure 2. Second Nash loop
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34 Hauray et al.1. IntroductionWe a
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36 Hauray et al.minimal time interv
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38 Hauray et al.From this last theo
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Approximation de l’opérateur d
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42 LemrabetLorsque δ → 0, l’é
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44 Lemrabetavec[∆ ∗ (δ)c = [I
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46 Lemrabet6. Bibliographie[1] N. B
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48 Jaafar-Belaid et al.1. Introduct
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50 Jaafar-Belaid et al.where f(ρ)
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52 Jaafar-Belaid et al.202040406060
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IIContrôleControl55
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58 Arfaoui1. IntroductionCe travail
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60 ArfaouiLe système adjoint assoc
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62 Arfaoui[3] H. ARFAOUI , « Comma
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64 Benzekri et al.1. Global control
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66 Benzekri et al.ẏ = ∂H∂x =
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0 1 2 3 4 5 668 Benzekri et al.32.5
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70 Akian et al.1. IntroductionWe co
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72 Akian et al.We prove that v t+δ
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.74 Akian et al.0.0−1.0Exact solu
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76 Bokanowski et al.1. General fram
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78 Bokanowski et al.One interesting
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80 Bokanowski et al.4. Numerical ex
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Sur les problèmes de contrôle opt
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84 Metouileur lorsque u est dans L
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86 MetouiThéorème 2 Soit ū ε le
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88 Metoui[5] L.S. HOU, S.S. RAVINDR
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Système d’information intégré
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Gestion et modélisation des ressou
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Random perturbations of reduced gra
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Global Optimization of Water Resour
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Global Optimization of Water Resour
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IVEquations DifférentiellesDiffere
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114 B. Abdelkader1. IntroductionTel
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116 B. AbdelkaderNotons que les con
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118 B. AbdelkaderAlors pour tout X
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120 Bouzahir1. IntroductionWe consi
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122 Bouzahirwhere the linear operat
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Goursat boundary value problem forh
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126 A. Guezane-Lakoud et al.3. Func
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128 A. Guezane-Lakoud et al.5. Exis
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ACP neuronale : application à l’
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132 Chitroubl’approximation de la
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134 Chitroubcompression égal à 3.
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136 Chitroub[4] J. P. Nougier, Mét
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138 Ferchichi et al.1. Introduction
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140 Ferchichi et al.soit T −1 = (
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142 Ferchichi et al.[4] G.I. BISCHI
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144 Hafidi1. IntroductionOn étudie
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146 Hafidia ) ∫ Ωpdx → +∞ qu
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148 HafidiLEMME 3.2. On a lim R(θ
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150 Lefebvre1. IntroductionSoit (X
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152 LefebvreOn peut aussi écrire q
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154 Lefebvre5. BibliographieD.R. Co
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156 Meskine1. IntroductionDans le p
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158 Meskinefaibles σ( ∏ L M ,
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160 Meskine[12] E. YA. KHRUSLOV, L.
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162 K. Saoudi1. IntroductionL’ét
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164 K. Saoudice qui montre que la s
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166 K. Saoudieton aboutit au résul
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168 K. Saoudietc 1 + c 2 = 1 ∫(u
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170 K. SaoudiRemarque 3.1. Du lemme
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172 K. Saoudiet∫v(t, x) dxΩest c
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VEstimation d’ErreursError Estima
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178 Achchab et al.1. IntroductionDu
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180 Achchab et al.such that}Xh 2 ={
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182 Achchab et al.[2] B.ACHCHAB, M.
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184 Alla et al.1. IntroductionLes e
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186 Alla et al.3. Position du probl
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IsoValue-0.00070533-0.000604452-0.0
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190 Achchab et al.1. IntroductionLe
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192 Achchab et al.où T hi/2 est le
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IsoValue0.000933570.002800710.00840
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196 El Dabaghi et al.1. Motivation
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198 El Dabaghi et al.0 ≤ λ i ≤
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200 El Dabaghi et al.Figure 4. Mail
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Residual error estimators for the t
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204 Kharrat et al.The step (3) need
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206 Kharrat et al.following estimat
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VIMécanique des FluidesFluid Mecha
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212 Abdelwahed et al.1. Introductio
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214 Abdelwahed et al.Ainsi, suivre
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216 Abdelwahed et al.Figure 3. Isov
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Procédure spectrale avec diagonali
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220 El Guarmah et al.où Ra et P r
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222 El Guarmah et al.M 10 20 25 35
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Simulation de l’onde de crue via
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226 El Dabaghi et al.de R 2 , de fr
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228 El Dabaghi et al.INRIA-MODULEFm
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INRIA-MODULEF230 El Dabaghi et al.I
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232 Amoura et al.1. IntroductionFor
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234 Amoura et al.where u = rot r ψ
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236 Amoura et al.CURRENT FUNCTION5
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238 Frih et al.1. IntroductionLa mo
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240 Frih et al.et dans la fracture,
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242 Frih et al.6. Bibliographie[1]
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244 Hasnaoui et al.1. IntroductionL
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246 Hasnaoui et al.On obtient alors
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248 Hasnaoui et al.5. Conclusion et
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250 Hernane-Boukari1. IntroductionW
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252 Hernane-Boukarianda 3 =−11 +
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254 Kloucha et al.1. Motivation et
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256 Kloucha et al.Si le domaine Λ
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INRIA-MODULEF0INRIA-MODULEF.250INRI
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Dimensions fractales : attracteurs
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262 Lamrini Uahabi et al.4. Attract
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264 Lamrini Uahabi et al.l 0 = λ
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266 Lamrini Uahabi et al.En conséq
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Numerical analysis for the problem
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270 El-Kyal et al.d(u) = 1 2(∇u +
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272 El-Kyal et al.Theorem 3.1 There
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274 Marchand et al.1. IntroductionD
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276 Marchand et al.On remarque en p
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278 Marchand et al.dans la formulat
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280 Mezali et al.1. Motivation et m
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282 Mezali et al.s’annulant sur c
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284 Mezali et al.Temps CPUTemps Ela
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R. Touihri & S. Ghnimi *Méthode de
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288 Touihri et al.Les équations go
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290 Touihri et al.Tol N It (Newt) t
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292 Touihri et al.5. Bibliographie[
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Décomposition de domaine pour un m
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DDM pour les fractures 297et−→
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DDM pour les fractures 299dont l’
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Arlequin method: Practical impacts
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Arlequin method 303of the Arlequin
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Arlequin method 3052.2. Penalized A
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Arlequin method 307refinements of t
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Reduction methods and uncertaintypr
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Reduction methods and uncertainty p
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Reduction methods and uncertainty p
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0.00e+00 1.00e−11 2.00e−11 3.00
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Homogénéisation d’un problème
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Problème de conduction-rayonnement
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Problème de conduction-rayonnement
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Equation d’Hamilton-Jacobi 3231.
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Equation d’Hamilton-Jacobi 325qui
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0Equation d’Hamilton-Jacobi 3272
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Détermination du nombre de région
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Simulation des courants de Foucault
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Une méthode d’accélération de
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Schéma SRNHS 3491. IntroductionUn
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Schéma SRNHS 3513. Application du
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Schéma SRNHS 3535. ConclusionLes r
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Smoothing and compressing surfaces
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Problème de convection en milieu p
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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0
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Condensation de masse pour la méth
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Condensation de masse 367montre que
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Condensation de masse 369Le terme h
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Condensation de masse 371Aet Qile f
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VIIIModèles CinétiquesKinetic Mod
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376 Ben Abdallah et al.1. Introduct
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378 Ben Abdallah et al.where B ∓
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380 Ben Abdallah et al.ture that th
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382 Degond et al.1. IntroductionOn
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384 Degond et al.q φ s (x, t)/(|q|
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386 Degond et al.Enfin l’opérate
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Les métaheuristiques : application
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Les métaheuristiques 391simulé fu
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Les métaheuristiques 393Fig. 1 Dia
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Les métaheuristiques 395Fig.4 : R
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Prices after capacity addition in m
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Multi-user elastic demand communica
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Shape optimization 4051. Introducti
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Shape optimization 4072.1. Variatio
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Shape optimization 409(a)(b)(c)(d)F
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Problèmes d’optimisation en éco
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Assimilation de données pour l’e
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Écart à la Réciprocité et ident
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Identification de fissures planes 4
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Algorithme de Kohn et Vogelius 4331
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Algorithme de Kohn et Vogelius 435l
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Algorithme de Kohn et Vogelius 4375
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Résolution d’un problème de Cau
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Problème inverse géométrique 445
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Identification de fissures 4511. In
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−0.5 −0.4 −0.3 −0.2 −0.1
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Application de l’algorithme deNeu
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Complétion de données en élastic
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Page 479 and 480: Analytic extensions on an annulus:a
Page 481 and 482: Analytic extensions on an annulus 4
Page 483 and 484: Analytic extensions on an annulus 4
Page 485 and 486: −1 −0.8 −0.6 −0.4 −0.2 0
Page 487 and 488: Cauchy problem for Laplace’s equa
Page 489 and 490: Cauchy problem for Laplace’s equa
Page 491: Cauchy problem for Laplace’s equa
Page 495 and 496: Modélisation asymptotique des onde
Page 497 and 498: Ondes de relief 479 uuuv 1 1 pu v
Page 499 and 500: Ondes de relief 481( )ˆeiZw( C e D
Page 501 and 502: Ondes de relief 4836. Bibliographie
Page 503 and 504: Régularisation pour l’aéroacous
Page 509 and 510: Miroir à retournement temporel 491
Page 515 and 516: Vibro-acoustique instationnaire 497
Page 517 and 518: Vibro-acoustique instationnaire 499
Page 519 and 520: Ondes dans les milieux poroélastiq
Page 525 and 526: The implicitly restarted Arnoldi me
Page 527: ¨§¨ §IRAM method 509To overcome
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Page 534 and 535: 516 Achchab1. IntroductionDans la m
Page 536 and 537: 518 AchchabNous montrons dans le pa
Page 538 and 539: 520 Achchab5. Bibliographie[1 ] AIE
Page 540 and 541: 522 Derbel et al.1. IntroductionLa
Page 542 and 543: 524 Derbel et al.On se propose de d
Page 544 and 545: A Stochastic partial differential e
Page 546 and 547: 528 El Saadidiffusion process to a
Page 548 and 549: 530 El Saadiwhere δ Xi(t) is the D
Page 550 and 551: 532 El Saadi5. References[1] A. L.
Page 552 and 553: 534 Ben Miled et al.1. Introduction
Page 554 and 555: 536 Ben Miled et al.nature de l’e
Page 556 and 557: 538 Ben Miled et al.Ensuite on étu
Page 558 and 559: Optimal spatial distribution of a b
Page 560 and 561: 542 Mchich et al.at time t. The com
Page 562 and 563: 544 Mchich et al.3∑where r = r i
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Page 568 and 569: 550 Ayadi1. IntroductionConsider a
Page 570 and 571: 552 AyadiFor all 0 ≤ p ≤ i, P p
Page 572 and 573: 554 AyadiFigure 3. The curves above
Page 574 and 575: Analyse mathématique et numérique
Page 576 and 577: 558 Chamekh et al.2. Modèle de tig
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560 Chamekh et al.Γ sr(s)r(σ)Figu
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Modélisation de l’effet dynamiqu
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564 RahmaniI 2 étant l’identité
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566 Rahmani• ( u 1δ1∫+ ,0u 1δ
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Résolution d’un systéme d’ela
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570 Saidoù u vérifie :n∑n∑j=1
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572 SaidEn effet :∣ u(y, s) 1+α
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Régularisation d’un problème d
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576 Achchab et al.On définit : F +
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578 Achchab et al.3. Estimation a p
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Modélisation d’un pieuR. Aboulai
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582 Aboulaich et al.Figure 1. Struc
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584 Aboulaich et al.5. Résultats n
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XIVThéorie des GraphesGraph Theory
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590 Hlaoui1. IntroductionMore and m
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592 Hlaoui3. The New Graph Represen
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594 Hlaoui1 1 2 3 4 1 1 3 411 421 4
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596 M. Nekri et al.1. IntroductionL
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598 M. Nekri et al.proposés. Ces m
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600 M. Nekri et al.cycle de longueu
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TAMTAM -Tunis- 2005 602
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El Guarmah, M., 218El Kacimi, A., 2
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