145 citations · 389 across the 9 of their papers we have counts for
5 papers · 1 filter
A Goal-Oriented Adaptive Discrete Empirical Interpolation Method
R. Stefanescu, A. Sandu
In this study we propose a-posteriori error estimation results to approximate the precision loss in quantities of interests computed using reduced order models. To generate the sur…
Multivariate predictions of local reduced-order-model errors and dimensions
Azam Moosavi, Razvan Stefanescu, Adrian Sandu
This paper introduces multivariate input-output models to predict the errors and bases dimensions of local parametric Proper Orthogonal Decomposition reduced-order models. We refer…
The Reduced-Order Hybrid Monte Carlo Sampling Smoother
Ahmed Attia, Razvan Stefanescu, Adrian Sandu
Hybrid Monte-Carlo (HMC) sampling smoother is a fully non-Gaussian four-dimensional data assimilation algorithm that works by directly sampling the posterior distribution formulate…
Efficient approximation of sparse Jacobians for time-implicit reduced order models
Răzvan Ştefănescu, Adrian Sandu
This paper introduces a sparse matrix discrete interpolation method to effectively compute matrix approximations in the reduced order modeling framework. The sparse algorithm devel…
Comparison of POD reduced order strategies for the nonlinear 2D Shallow Water Equations
Răzvan Ştefănescu, Adrian Sandu, Ionel M. Navon
This paper introduces tensorial calculus techniques in the framework of Proper Orthogonal Decomposition (POD) to reduce the computational complexity of the reduced nonlinear terms.…