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38 Hauray et al.From this last theorem, it is easy to deduce the main result of this paper, which readsTheorem 3.3 Consider a time T and sequence µ N (t) corresponding to solutions to (1)such that R(0), K(0) and m(0) are bounded uniformly in N. Then any weak limit f ofµ N (t) in L ∞ ([0, T ], M 1 (R 2d )) belongs to L ∞ ([0, T ], L 1 ∩ L ∞ (R 2d )), has compactsupport and is a solution to (3).Of course the main limitation of our results is the condition α < 1 and the main openquestion is to know what happens when α ≥ 1. However this condition is not onlytechnical and new ideas will be needed to prove something for α ≥ 1. It would alsobe interesting to extend our result to more complicated forces like the ones found in theformal derivation of [9].4. References[1] J. Batt, N-Particle approximation to the nonlinear Vlasov-Poisson system, Preprint.[2] J. Batt, G. Rein, Global classical solutions of the periodic Vlasov-Poisson system in threedimensions, C.R. Acad. Sci. Paris, 313, serie 1, pp. 411-416, 1991.[3] W. Braun, K. Hepp,The Vlasov dynamics and its fluctuations in the 1/N limit of interactingclassical particles, Comm. Math. Phys., 56, no. 2, pp. 101–113, 1977.[4] R. L. Dobrušin, Vlasov equations, Funktsional. Anal. i Prilozhen., 13, pp 48–58, 1979.[5] R.T. Glassey, The Cauchy problem in kinetic theory, Philadelphia PA, SIAM, 1996.[6] J. Goodman, T.Y. Hou and J. Lowengrub, Convergence of the point vortex method for the 2-DEuler equations, Comm. Pure Appl. Math., 43, pp 415–430, 1990.[7] E. Horst, On the classical solutions of the initial value problem for the unmodified non-linearVlasov equation I, Math. Meth. Appl. Sci., 3, pp 229-248, 1981.[8] E. Horst, On the classical solutions of the initial value problem for the unmodified non-linearVlasov equation II, Math. Meth. Appl. Sci., 4, pp 19-32, 1982.[9] P.E. Jabin and B. Perthame, Notes on mathematical problems on the dynamics of dispersedparticles interacting through a fluid, Modelling in applied sciences, 111–147, Model. Simul.Sci. Eng. Technol., Birkhauser Boston, 2000.[10] P.L. Lions, B. Perthame, Propagation of moments and regularity for the 3-dimensionalVlasov-Poisson System, Invent. Math., 105, pp. 415-430, 1991.[11] H. Neunzert, J. Wick, Theoretische und numerische Ergebnisse zur nichtlinearen Vlasov-Gleichung. Numerische Lösung nichtlinearer partieller Differential- und Integrodifferentialgleichungen(Tagung, Math. Forschungsinst., Oberwolfach, 1971), pp. 159–185. Lecture Notes inMath., Vol. 267, Springer, Berlin, 1972.[12] K. Pfaffelmoser, Global classical solutions of the Vlasov-Poisson system in three dimensionsfor general initial data, J. Diff. Eq., 95, pp 281-303, 1992.[13] J. Schaeffer, Global existence of smooth solutions to the Vlasov-Poisoon system in three dimensions,Comm. P.D.E., 16, pp 1313-1335, 1991.TAMTAM –Tunis– 2005
Approximation of the Vlasov equations 39[14] H. Spohn, Large scale dynamics of interacting particles, Springer-Verlag Berlin, 1991.[15] H.D. Victory, jr., E.J. Allen, The convergence theory of particle-in-cell methods for multidimensionalVlasov-Poisson systems, SIAM J. Numer. Anal., 28, pp 1207–1241, 1991.[16] S. Wollman, On the approximation of the Vlasov-Poisson system by particles methods, SIAMJ. Numer. Anal., 37, pp 1369–1398, 2000.[17] S. Wollman, Global in Time Solutions to the Three-Dimensional Vlasov-Poisson System, J.Math. Anal. Appl., 176, no. 1, 76–91, 1993.TAMTAM –Tunis– 2005
Page 5 and 6: PréfaceOrganiser la seconde éditi
Page 7: Comité d’organisationMohamed Abd
Page 11 and 12: SOMMAIRE / CONTENTSI Conférences I
Page 13 and 14: Equilibre dans un système dynamiqu
Page 15 and 16: Arlequin method: Practical impacts
Page 17 and 18: Application de la méthode de Gauss
Page 19: Conférences InvitéesInvited Prese
Page 22 and 23: 4 Amara et al.1. IntroductionWe are
Page 24 and 25: 6 Amara et al.2.2. The nonlinear Na
Page 26 and 27: 8 Amara et al.4. Numerical resultsW
Page 28 and 29: 10 Auger et al.1. Introduction :Dan
Page 30 and 31: 12 Auger et al.La condition de stab
Page 32 and 33: 14 Auger et al.3.1. Taux de migrati
Page 34 and 35: 16 Auger et al.L’équilibre rapid
Page 36 and 37: 18 Auger et al.[7] BRAVO DE LA PARR
Page 38 and 39: Quelques aspects mathématiques etn
Page 40 and 41: 22 El Alaoui Talibi M.- Modèle d
Page 42 and 43: 24 El Alaoui Talibi M.[3] M. Chamba
Page 44 and 45: 26 Habbal1. IntroductionAngiogenesi
Page 46 and 47: 28 HabbalThe pressure drop denoted
Page 48 and 49: 30 Habbaltheorem 1 There exists a N
Page 50 and 51: 32 HabbalFigure 2. Second Nash loop
Page 52 and 53: 34 Hauray et al.1. IntroductionWe a
Page 54 and 55: 36 Hauray et al.minimal time interv
Page 58 and 59: Approximation de l’opérateur d
Page 60 and 61: 42 LemrabetLorsque δ → 0, l’é
Page 62 and 63: 44 Lemrabetavec[∆ ∗ (δ)c = [I
Page 64 and 65: 46 Lemrabet6. Bibliographie[1] N. B
Page 66 and 67: 48 Jaafar-Belaid et al.1. Introduct
Page 68 and 69: 50 Jaafar-Belaid et al.where f(ρ)
Page 70 and 71: 52 Jaafar-Belaid et al.202040406060
Page 73: IIContrôleControl55
Page 76 and 77: 58 Arfaoui1. IntroductionCe travail
Page 78 and 79: 60 ArfaouiLe système adjoint assoc
Page 80 and 81: 62 Arfaoui[3] H. ARFAOUI , « Comma
Page 82 and 83: 64 Benzekri et al.1. Global control
Page 84 and 85: 66 Benzekri et al.ẏ = ∂H∂x =
Page 86 and 87: 0 1 2 3 4 5 668 Benzekri et al.32.5
Page 88 and 89: 70 Akian et al.1. IntroductionWe co
Page 90 and 91: 72 Akian et al.We prove that v t+δ
Page 92 and 93: .74 Akian et al.0.0−1.0Exact solu
Page 94 and 95: 76 Bokanowski et al.1. General fram
Page 96 and 97: 78 Bokanowski et al.One interesting
Page 98 and 99: 80 Bokanowski et al.4. Numerical ex
Page 100 and 101: Sur les problèmes de contrôle opt
Page 102 and 103: 84 Metouileur lorsque u est dans L
Page 104 and 105: 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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Complétion de données en élastic
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Analytic extensions on an annulus:a
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Analytic extensions on an annulus 4
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Analytic extensions on an annulus 4
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−1 −0.8 −0.6 −0.4 −0.2 0
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Cauchy problem for Laplace’s equa
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Modélisation asymptotique des onde
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Ondes de relief 479 uuuv 1 1 pu v
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Ondes de relief 481( )ˆeiZw( C e D
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Ondes de relief 4836. Bibliographie
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Régularisation pour l’aéroacous
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Miroir à retournement temporel 491
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Vibro-acoustique instationnaire 497
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Vibro-acoustique instationnaire 499
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Ondes dans les milieux poroélastiq
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The implicitly restarted Arnoldi me
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¨§¨ §IRAM method 509To overcome
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IRAM method 511The application of t
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XIIScience du VivantLife Science513
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516 Achchab1. IntroductionDans la m
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518 AchchabNous montrons dans le pa
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520 Achchab5. Bibliographie[1 ] AIE
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522 Derbel et al.1. IntroductionLa
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524 Derbel et al.On se propose de d
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A Stochastic partial differential e
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528 El Saadidiffusion process to a
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530 El Saadiwhere δ Xi(t) is the D
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532 El Saadi5. References[1] A. L.
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534 Ben Miled et al.1. Introduction
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536 Ben Miled et al.nature de l’e
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538 Ben Miled et al.Ensuite on étu
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Optimal spatial distribution of a b
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542 Mchich et al.at time t. The com
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544 Mchich et al.3∑where r = r i
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XIIIStructureStructure547
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550 Ayadi1. IntroductionConsider a
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552 AyadiFor all 0 ≤ p ≤ i, P p
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554 AyadiFigure 3. The curves above
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Analyse mathématique et numérique
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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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