LEADER 04528nam 22006135 450 001 9910483609303321 005 20200730120341.0 010 $a3-030-48721-0 024 7 $a10.1007/978-3-030-48721-8 035 $a(CKB)4100000011363808 035 $a(DE-He213)978-3-030-48721-8 035 $a(MiAaPQ)EBC6274714 035 $a(PPN)269149651 035 $a(EXLCZ)994100000011363808 100 $a20200730d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aQuantification of Uncertainty: Improving Efficiency and Technology $eQUIET selected contributions /$fedited by Marta D'Elia, Max Gunzburger, Gianluigi Rozza 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XI, 282 p. 113 illus., 90 illus. in color.) 225 1 $aLecture Notes in Computational Science and Engineering,$x1439-7358 ;$v137 311 $a3-030-48720-2 327 $a1. 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. 330 $aThis book explores four guiding themes ? reduced order modelling, high dimensional problems, efficient algorithms, and applications ? by reviewing recent algorithmic and mathematical advances and the development of new research directions for uncertainty quantification in the context of partial differential equations with random inputs. Highlighting the most promising approaches for (near-) future improvements in the way uncertainty quantification problems in the partial differential equation setting are solved, and gathering contributions by leading international experts, the book?s content will impact the scientific, engineering, financial, economic, environmental, social, and commercial sectors. 410 0$aLecture Notes in Computational Science and Engineering,$x1439-7358 ;$v137 606 $aComputer mathematics 606 $aApplied mathematics 606 $aEngineering mathematics 606 $aComputer simulation 606 $aComputational Mathematics and Numerical Analysis$3https://scigraph.springernature.com/ontologies/product-market-codes/M1400X 606 $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 615 0$aComputer mathematics. 615 0$aApplied mathematics. 615 0$aEngineering mathematics. 615 0$aComputer simulation. 615 14$aComputational Mathematics and Numerical Analysis. 615 24$aMathematical and Computational Engineering. 615 24$aSimulation and Modeling. 676 $a519.54 702 $aD'Elia$b Marta$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGunzburger$b Max$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRozza$b Gianluigi$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483609303321 996 $aQuantification of Uncertainty: Improving Efficiency and Technology$92147585 997 $aUNINA