03079nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001600153072001700169072002300186072001600209245019600225250001800421264006900439300006500508336002600573337002600599338003600625347002400661490007800685505058000763520050201343650002401845650002201869650002401891650002201915700014601937700008002083700007802163700007602241710003402317773002602351776003602377776003602413776003602449830007802485856004602563978-3-031-55060-7DE-He21320251030112328.0cr nn 008mamaa240624s2024    sz |    s    |||| 0|eng d  a97830315506077 a10.1007/978-3-031-55060-72doi 4aQA297-299.4 7aPBKS2bicssc 7aMAT0410002bisacsh 7aPBKS2thema10aReduction, Approximation, Machine Learning, Surrogates, Emulators and Simulatorsh[electronic resource] :bRAMSES /cedited by Gianluigi Rozza, Giovanni Stabile, Max Gunzburger, Marta D'Elia.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aX, 259 p. 151 illus., 148 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aLecture Notes in Computational Science and Engineering,x2197-7100 ;v1510 aShafqat Ali, Francesco Ballarin and Gianluigi Rozza: An online stabilization method for parametrized viscous flows -- Margarita Chasapi, Pablo Antolin, Annalisa Buffa: Reduced order modelling of nonaffine problems on parameterized NURBS multipatch geometries -- Anton Dereventsov, Joseph Daws, Jr., and Clayton G. Webster: Offline Policy Comparison under Limited Historical Agent-Environment Interactions -- Julien Genovese, Francesco Ballarin, Gianluigi Rozza and Claudio Canuto: Weighted reduced order methods for uncertainty quantification in computational fluid dynamics.  aThis volume is focused on the review of recent algorithmic and mathematical advances and the development of new research directions for Mathematical Model Approximations via RAMSES (Reduced order models, Approximation theory, Machine learning, Surrogates, Emulators, Simulators) in the setting of parametrized partial differential equations also with sparse and noisy data in high-dimensional parameter spaces. The book is a valuable resource for researchers, as well as masters and Ph.D students. 0aNumerical analysis. 0aMachine learning.14aNumerical Analysis.24aMachine Learning.1 aRozza, Gianluigi.eeditor.0(orcid)0000-0002-0810-88121https://orcid.org/0000-0002-0810-88124edt4http://id.loc.gov/vocabulary/relators/edt1 aStabile, Giovanni.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aGunzburger, Max.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt1 aD'Elia, Marta.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303155059108iPrinted edition:z978303155061408iPrinted edition:z9783031550621 0aLecture Notes in Computational Science and Engineering,x2197-7100 ;v15140uhttps://doi.org/10.1007/978-3-031-55060-7