00542nas a2200121 4500008004100000245009900041210006900140100002000209700002400229700002100253700002200274856012400296 2021 eng d00aAn artificial neural network approach to bifurcating phenomena in computational fluid dynamics0 aartificial neural network approach to bifurcating phenomena in c1 aPichi, Federico1 aBallarin, Francesco1 aRozza, Gianluigi1 aHesthaven, Jan, S uhttps://math.sissa.it/publication/artificial-neural-network-approach-bifurcating-phenomena-computational-fluid-dynamics01364nam a2200229 4500008004100000020002200041022001400063245008400077210006900161250000600230260002600236300000800262520053600270653003000806653002800836653004800864653004500912100002200957700002100979700002001000856011401020 2015 eng d a978-3-319-22469-5 a2191-820100aCertified Reduced Basis Methods for Parametrized Partial Differential Equations0 aCertified Reduced Basis Methods for Parametrized Partial Differe a1 aSwitzerlandbSpringer a1353 a
This book provides a thorough introduction to the mathematical and algorithmic aspects of certified reduced basis methods for parametrized partial differential equations. Central aspects ranging from model construction, error estimation and computational efficiency to empirical interpolation methods are discussed in detail for coercive problems. More advanced aspects associated with time-dependent problems, non-compliant and non-coercive problems and applications with geometric variation are also discussed as examples.
10aa posteriori error bounds10aempirical interpolation10aparametrized partial differential equations10areduced basis methods, greedy algorithms1 aHesthaven, Jan, S1 aRozza, Gianluigi1 aStamm, Benjamin uhttps://math.sissa.it/publication/certified-reduced-basis-methods-parametrized-partial-differential-equations