145 citations · 389 across the 9 of their papers we have counts for
10 papers
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…
Hybrid optimization and Bayesian inference techniques for a non-smooth radiation detection problem
Razvan Stefanescu, Kathleen Schmidt, Jason Hite +2
In this investigation, we propose several algorithms to recover the location and intensity of a radiation source located in a simulated 250 m x 180 m block in an urban center based…
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 Construction of Local Parametric Reduced Order Models Using Machine Learning Techniques
Azam Moosavi, Razvan Stefanescu, Adrian Sandu
Reduced order models are computationally inexpensive approximations that capture the important dynamical characteristics of large, high-fidelity computer models of physical systems…
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.…
POD/DEIM Reduced-Order Strategies for Efficient Four Dimensional Variational Data Assimilation
Răzvan Ştefănescu, Adrian Sandu, Ionel Michael Navon
This work studies reduced order modeling (ROM) approaches to speed up the solution of variational data assimilation problems with large scale nonlinear dynamical models. It is show…
1D+4D-VAR data assimilation of lightning with WRFDA system using nonlinear observation operators
Razvan Stefanescu, I. Michael Navon, Henry Fuelberg +1
This paper addresses the impact of assimilating data from the Earth Networks Total Lightning Network (ENTLN) during two cases of severe weather. Data from the ENTLN serve as a subs…
POD/DEIM Nonlinear model order reduction of an ADI implicit shallow water equations model
Razvan Stefanescu, Ionel Michael Navon
In the present paper we consider a 2-D shallow-water equations (SWE) model on a -plane solved using an alternating direction fully implicit (ADI) finite-difference scheme on a r…