Cantitate/Preț
Produs
Total produse
Vezi coșul
Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions de Marta D'Elia

Quantification of Uncertainty: Improving Efficiency and Technology: QUIET selected contributions: Lecture Notes in Computational Science and Engineering, cartea 137

Editat de Marta D'Elia, Max Gunzburger, Gianluigi Rozza
en Limba Engleză Paperback – aug 2021
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.

Citește tot Restrânge

Toate formatele și edițiile

Toate formatele și edițiile Preț Express
Paperback (1) 62743 lei  6-8 săpt.
  Springer International Publishing – aug 2021 62743 lei  6-8 săpt.
Hardback (1) 63354 lei  6-8 săpt.
  Springer International Publishing – 31 iul 2020 63354 lei  6-8 săpt.

Din seria Lecture Notes in Computational Science and Engineering

Preț: 62743 lei

Preț vechi: 73815 lei
-15% Nou

Puncte Express: 941
Preț estimativ în valută:
12012€ • 12485$ • 9959£

Carte tipărită la comandă

Livrare economică 06-20 februarie 25

 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă
Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783030487232
ISBN-10: 3030487237
Ilustrații: XI, 282 p. 113 illus., 90 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.42 kg
Ediția:1st ed. 2020
Editura: Springer International Publishing
Colecția Springer
Seria Lecture Notes in Computational Science and Engineering

Locul publicării:Cham, Switzerland

Cuprins

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. Bulté, M. et al., A practical example for the non-linear Bayesian filtering of model parameters.

Notă biografică

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. 

Textul de pe ultima copertă

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.

Caracteristici

Offers 4 crucial modern topics and their synergistic interaction: model order reduction, efficient solvers, high dimensional approximation and applications Includes contributions, upon invitation, from world experts Provides high quality, inspiring and innovative chapters spanning a broad class of critical topics