Documents  65N15 | enregistrements trouvés : 29

- x; 215 p.
ISBN 978-3-0348-0630-5

International series of numerical mathematics , 0164

Localisation : Colloque 1er étage (MARI)

EDP # équation aux dérivées partielles # optimisation mathématique # méthode numérique pour le calcul des variations

49-XX ; 35-XX ; 35J25 ; 35K20 ; 35R60 ; 49J20 ; 49K20 ; 49M05 ; 49M15 ; 49M25 ; 65K10 ; 65M60 ; 65N15 ; 65N30

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- 386 p.
ISBN 978-0-8218-3662-0

Contemporary mathematics , 0383

Localisation : Collection 1er étage

analyse numérique # méthode des éléments finis # EDP # convergence des méthodes numériques # génération de maillage # erreur # méthode multigrille # mécanique des fluides # équation de Navier-Stokes

65M12 ; 65M50 ; 65N12 ; 65N15 ; 65N30 ; 65N50 ; 65N55 ; 76-02 ; 76D05

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- 403 p.
ISBN 978-0-8218-3017-8

Proceedings of the Steklov institute of mathematics , 0117

Localisation : Collection 1er étage

24AXX ; 35-02 ; 41A08 ; 41A63 ; 65N15

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- 179 p.
ISBN 978-0-387-15234-9

Lecture notes in mathematics , 1129

Localisation : Collection 1er étage

35J25 ; 41A50 ; 41A63 ; 65N15 ; 65N30

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Research schools

Many problems in computational science require the approximation of a high-dimensional function from limited amounts of data. For instance, a common task in Uncertainty Quantification (UQ) involves building a surrogate model for a parametrized computational model. Complex physical systems involve computational models with many parameters, resulting in multivariate functions of many variables. Although the amount of data may be large, the curse of dimensionality essentially prohibits collecting or processing enough data to reconstruct such a function using classical approximation techniques. Over the last five years, spurred by its successful application in signal and image processing, compressed sensing has begun to emerge as potential tool for surrogate model construction UQ. In this talk, I will give an overview of application of compressed sensing to high-dimensional approximation. I will demonstrate how the appropriate implementation of compressed sensing overcomes the curse of dimensionality (up to a log factor). This is based on weighted l1 regularizers, and structured sparsity in so-called lower sets. If time, I will also discuss several variations and extensions relevant to UQ applications, many of which have links to the standard compressed sensing theory. These include dealing with corrupted data, the effect of model error, functions defined on irregular domains and incorporating additional information such as gradient data. I will also highlight several challenges and open problems. Many problems in computational science require the approximation of a high-dimensional function from limited amounts of data. For instance, a common task in Uncertainty Quantification (UQ) involves building a surrogate model for a parametrized computational model. Complex physical systems involve computational models with many parameters, resulting in multivariate functions of many variables. Although the amount of data may be large, the curse ...

41A05 ; 41A10 ; 65N12 ; 65N15 ; 94A12

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Research talks

Parametrized PDE (Partial Differential Equation) Apps are PDE solvers which satisfy stringent per-query performance requirements: less-than or approximate 5-second problem specification time; less-than or approximate 5-second problem solution time, field and outputs; less-than or approximate 5% solution error, specified metrics; less-than or approximate 5-second solution visualization time. Parametrized PDE apps are relevant in many-query, real-time, and interactive contexts such as design, parameter estimation, monitoring, and education.
In this talk we describe and demonstrate a PDE App computational methodology. The numerical approach comprises three ingredients: component => system synthesis, formulated as a static-condensation procedure; model order reduction, informed by evanescence arguments at component interfaces (port reduction) and low-dimensional parametric manifolds in component interiors (reduced basis techniques); and parallel computation, implemented in a cloud environment. We provide examples in acoustics and also linear elasticity. Parametrized PDE (Partial Differential Equation) Apps are PDE solvers which satisfy stringent per-query performance requirements: less-than or approximate 5-second problem specification time; less-than or approximate 5-second problem solution time, field and outputs; less-than or approximate 5% solution error, specified metrics; less-than or approximate 5-second solution visualization time. Parametrized PDE apps are relevant in many-query, ...

65N30 ; 65N15 ; 65M60 ; 65M15 ; 93B50

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Research talks

We review how to bound the error between the unknown weak solution of a PDE and its numerical approximation via a fully computable a posteriori estimate. We focus on approximations obtained at an arbitrary step of a linearization (Newton-Raphson, fixed point, ...) and algebraic solver (conjugate gradients, multigrid, domain decomposition, ...). Identifying the discretization, linearization, and algebraic error components, we design local stopping criteria which keep them in balance. This gives rise to a fully adaptive inexact Newton method. Numerical experiments are presented in confirmation of the theory. We review how to bound the error between the unknown weak solution of a PDE and its numerical approximation via a fully computable a posteriori estimate. We focus on approximations obtained at an arbitrary step of a linearization (Newton-Raphson, fixed point, ...) and algebraic solver (conjugate gradients, multigrid, domain decomposition, ...). Identifying the discretization, linearization, and algebraic error components, we design local ...

65N15 ; 65N22 ; 65Y05

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Research talks;Partial Differential Equations

49M15 ; 65N15 ; 65N30 ; 76TXX

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- xxiv; 497 p.
ISBN 978-3-319-79041-1

Mathématiques & applications , 0082

Localisation : Collection 1er étage

méthode de discrétisation du gradient # schéma de gradients # équation aux dérivées partielles elliptiques # équation aux dérivées partielles paraboliques # analyse de convergence uniforme # théorème d'Aubin-Simon discret # convergence par compacité # estimation des erreurs

65M06 ; 65M08 ; 65M12 ; 65M15 ; 65M60 ; 65N06 ; 65N08 ; 65N12 ; 65N15 ; 65N30

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- xviii; 267 p.
ISBN 978-1-84265-913-7

Localisation : Ouvrage RdC (EFFE)

nombre de condition # nombre de condition effectif # analyse de la stabilité # équation de Poisson # limite d'erreur # élimination de Gauss # factorisation QR # méthode des différences finies

65N06 ; 65F05 ; 65F35 ; 65N15 ; 35J05

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- xvi; 470 p.
ISBN 978-3-540-71040-0

Springer series in computational mathematics , 0041

Localisation : Ouvrage RdC (SHEN)

théorie spectrale # méthode spectrale # équation aux dérivées partielles # convergence # analyse d'erreur # équation différentielle d'ordre supérieur # domaine illimité # codes de Matlab

65M70 ; 65M12 ; 65N15 ; 65N22 ; 65F05 ; 35J25 ; 35J40 ; 35K15 ; 42C05 ; 65N35 ; 65-02 ; 65M15 ; 65N12

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- xiv; 543 p.
ISBN 978-3-319-5082-9

International series of numerical mathematics , 0165

Localisation : Ouvrage RdC (TREN)

optimisation sous contraintes # optimisation topologique # réduction de modèle # discrétisation # contrôle optimal

35K55 ; 49J20 ; 49K20 ; 49M25 ; 65K10 ; 65M60 ; 65N15 ; 65N30 ; 76D55 ; 90C30 ; 90-06 ; 49-06 ; 90C90

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- xx; 393 p.
ISBN 978-0-19-967942-3

Numerical mathematics and scientific computation

Localisation : Ouvrage RdC (VERF)

estimation d'erreur a posteriori # méthode de discrétisation auto-adaptive # méthode des éléments finis # équations elliptiques linéaires # équations elliptiques non-linéaires # équations paraboliques linéaires # équations paraboliques non-linéaires # méthode des volumes finis # adaptation de maillage # équation de Poisson

65N15 ; 65M15 ; 65-02 ; 65N30 ; 65N50 ; 65M60 ; 65M08 ; 35J25 ; 35J60 ; 35K20 ; 35K55 ; 65M50 ; 65N08

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- xvii; 385 p.
ISBN 978-3-642-33286-9

Texts in computational science and engineering , 0010

Localisation : Ouvrage RdC (LARS)

analyse numérique # équations aux dérivées partielles # conception par ordinateur # CAD CAE # MATLAB # méthode des éléments finis # approximation polynomiale

65N30 ; 65N15 ; 65M60 ; 65M15 ; 65-02 ; 74S05 ; 76M10 ; 35J25 ; 35Q35 ; 35Q60 ; 65Y15 ; 65F50 ; 65F10

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- xvii; 384 p.
ISBN 978-3-642-22979-4

Mathématiques & applications , 0069

Localisation : Collection 1er étage

méthode de Galerkin # fonction discontinue # mathématiques de l'ingénieur # problème linéaire # EDP # système de Friedrich # diffusion # problème non-linéaire # loie de conservation # problème bien posé # convergence # stabilité # équation de Navier-Stokes # affinage du Mesh

65N30 ; 65M60 ; 65N08 ; 65N12 ; 65N15 ; 65M12 ; 65M15 ; 76M10 ; 76M12 ; 65-02 ; 76R50 ; 76D05 ; 35Q30 ; 65N50 ; 65M50

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- xi; 622 p.
ISBN 978-3-0348-0132-4

International series of numerical mathematics , 0160

Localisation : Ouvrage RdC (CONS)

analyse numérique # calcul de variations # EDP # méthode aux différences finies # programmation non-linéaire

35K55 ; 49J20 ; 49K20 ; 49M25 ; 65K10 ; 65M60 ; 65N15 ; 65N30 ; 76D55 ; 90C30 ; 35-06 ; 35R99 ; 90C90

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- vi; 345 p.
ISBN 978-3-7643-8922-2

International series of numerical mathematics , 0158

Localisation : Ouvrage RdC (OPTI)

contrôle optimal # EDP # feedback

49-XX ; 35-XX ; 14J70 ; 26A16 ; 30F45 ; 35A15 ; 35B37 ; 35Bxx ; 35D05 ; 35J85 ; 35L05 ; 35L55 ; 35L99 ; 35Q30 ; 35Q35 ; 35Q40 ; 47J40 ; 49J20 ; 65J15 ; 65K10 ; 49K20 ; 49L20 ; 65N15 ; 49Q10 ; 70k70 ; 73K12 ; 74B05 ; 74K20 ; 74K25 ; 74K30 ; 74P05 ; 76D05 ; 76D07 ; 76N10 ; 76N15 ; 78A55 ; 80A20 ; 90C22 ; 90C25 ; 90C31 ; 90C90 ; 93A30 ; 93B05 ; 93B12 ; 93C20 ; 93D20 ; 93-XX ; 76D55 ; 35J65 ; 93B07

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- xi; 316 p.
ISBN 978-3-11-019153-0

Radon Series on Computational and Applied Mathematics , 0004

Localisation : Ouvrage RdC (REPI)

EDP # équation différentielle # estimation d'erreur à postériori # équation de poisson # problème de diffusion # elaticity # fluide incompressible # problème non linéaire

35-02 ; 00-02 ; 35J05 ; 35K05 ; 65M15 ; 65N15 ; 74BXX ; 76Dxx

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- 330 p.
ISBN 978-3-540-41339-4

Computational fluid and solid mechanics

Localisation : Ouvrage RdC (CHAP)

modèle de coque # comportement asymptotique # méthode numérique finie # approximation par éléments finis # coque # analyse non-linéaire # épaisseur

74K25 ; 65N30 ; 65N15

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