01480nas a2200145   4500008004100000022001400041245010300055210006900158300001100227490000800238520092500246100002201171700001801193856012301211       2020                        eng d  a0045-793000aBayesian identification of a projection-based reduced order model for computational fluid dynamics0 aBayesian identification of a projectionbased reduced order model  a1044770 v2013 aIn this paper we propose a Bayesian method as a numerical way to correct and stabilise projection-based reduced order models (ROM) in computational fluid dynamics problems. The approach is of hybrid type, and consists of the classical proper orthogonal decomposition driven Galerkin projection of the laminar part of the governing equations, and Bayesian identification of the correction term mimicking both the turbulence model and possible ROM-related instabilities given the full order data. In this manner the classical ROM approach is translated to the parameter identification problem on a set of nonlinear ordinary differential equations. Computationally the inverse problem is solved with the help of the Gauss-Markov-Kalman smoother in both ensemble and square-root polynomial chaos expansion forms. To reduce the dimension of the posterior space, a novel global variance based sensitivity analysis is proposed.1 aStabile, Giovanni1 aRosic, Bojana  uhttps://www.math.sissa.it/publication/bayesian-identification-projection-based-reduced-order-model-computational-fluid