Personnaliser

OK
Quantification Of Uncertainty: Improving Efficiency And Technology Format Relié

Quantification of Uncertainty: Improving Efficiency and Technology -

Note : 0

0 avis
  • Soyez le premier à donner un avis

Vous en avez un à vendre ?

Vendez-le-vôtre

135,67 €

Produit Neuf

  • Ou 33,92 € /mois

    • En savoir plus
    • Livraison à 0,01 €
    • Livré entre le 4 et le 11 mai
    Voir les modes de livraison

    RiaChristie

    PRO Vendeur favori

    4,9/5 sur + de 1 000 ventes

    Brand new, In English, Fast shipping from London, UK; Tout neuf, en anglais, expédition rapide depuis Londres, Royaume-Uni;ria9783030487201_dbm

    Publicité
     
    Vous avez choisi le retrait chez le vendeur à
    • Payez directement sur Rakuten (CB, PayPal, 4xCB...)
    • Récupérez le produit directement chez le vendeur
    • Rakuten vous rembourse en cas de problème
    Terminer ma commande

    Gratuit et sans engagement

    Félicitations !

    Nous sommes heureux de vous compter parmi nos membres du Club Rakuten !

    En savoir plus

    TROUVER UN MAGASIN

      Retour

      Horaires

          Note :


          Avis sur Quantification Of Uncertainty: Improving Efficiency And Technology Format Relié  - Livre

          Note : 0 0 avis sur Quantification Of Uncertainty: Improving Efficiency And Technology Format Relié  - Livre

          Les avis publiés font l'objet d'un contrôle automatisé de Rakuten.


          Présentation Quantification Of Uncertainty: Improving Efficiency And Technology Format Relié

           - Livre

          Livre - 01/07/2020 - Relié - Langue : Anglais

          . .

        • Editeur : Springer International Publishing Ag
        • Langue : Anglais
        • Parution : 01/07/2020
        • Format : Moyen, de 350g à 1kg
        • Nombre de pages : 296
        • Expédition : 612
        • Dimensions : 24.1 x 16.0 x 2.2
        • ISBN : 3030487202



          • Résumé :
          This book explores four guiding themes ? reduced order modelling, high dimensional problems, efficient algorithms, and applications ? by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book?s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors.

          Biographie:

          Dr. Marta D'Elia is a staff member at Sandia National Laboratories. She graduated with honors in Mathematical Engineering at Politecnico di Milano, 2007, and she has a Phd in Applied Mathematics from Emory University, 2011. She was a postdoctoral fellow at Florida State University from 2012 to 2014. Her research deals with computational science and engineering, and it is mainly focused on modeling and simulation of nonlocal problems. She authored more than 30 research papers, she is the principal investigator of a Laboratory Directed R&D grant, associate editor of the SIAM Journal on Scientific Computing and organizer of several international conferences.

           

          Max Gunzburger, a Distinguished Professor at Florida State University, has advised 46 PhD students and 36 postdocs, published over 300 journal articles, conducted research funded by several US agencies, consulted for government and private labs, and served as EIC of two SIAM journals. His research interests include numerical analysis, finite elements, control, grid generation, and differential and integral equations with applications in mechanics, diffusion, climate, superconductivity, subsurface flows, etc.

          Gianluigi Rozza is a Professor of Numerical Analysis and Scientific Computing at SISSA, the International School for Advanced Studies, in Trieste, Italy, where he is a lecturer and coordinator of the SISSA doctoral program in Mathematical Analysis, Modelling and Applications, and director delegate for innovation, valorisation of research and technology transfer. His research chiefly focuses on developing reduced order methods. The author of more than 120 scientific publications, Principal Investigator of the European Research Council AROMA-CFD project. Winner of the 2004 Bill Morton CFD Prize (Oxford University); ECCOMAS Phd Award 2006; Springer CSE prize in 2009; and ECCOMAS Jacques Louis Lions Award in 2014. 


          Sommaire:
          1. Adeli, E. et al., Effect of Load Path on Parameter Identification for Plasticity Models using Bayesian Methods.- 2. Brugiapaglia S., A compressive spectral collocation method for the diffusion equation under the restricted isometry property.- 3. D'Elia, M. et al., Surrogate-based Ensemble Grouping Strategies for Embedded Sampling-based Uncertainty Quantification.- 4. Afkham, B.M. et al., Conservative Model Order Reduction for Fluid Flow.- 5. Clark C.L. and Winter C.L., A Semi-Markov Model of Mass Transport through Highly Heterogeneous Conductivity Fields.- 6. Matthies, H.G., Analysis of Probabilistic and Parametric Reduced Order Models.- 7. Carraturo, M. et al., Reduced Order Isogeometric Analysis Approach for PDEs in Parametrized Domains.- 8. Boccadifuoco, A. et al., Uncertainty quantification applied to hemodynamic simulations of thoracic aorta aneurysms: sensitivity to inlet conditions.- 9. Anderlini, A.et al., Cavitation model parameter calibration for simulations of three-phase injector flows.- 10. Hijazi, S. et al., Non-Intrusive Polynomial Chaos Method Applied to Full-Order and Reduced Problems in Computational Fluid Dynamics: a Comparison and Perspectives.- 11. Bult?, M. et al., A practical example for the non-linear Bayesian filtering of model parameters.