Approximate deconvolution reduced order modeling
This paper proposes a large eddy simulation reduced order model (LES-ROM) framework
for the numerical simulation of convection-dominated flows. In this LES-ROM framework, the …
for the numerical simulation of convection-dominated flows. In this LES-ROM framework, the …
Multivariate predictions of local reduced‐order‐model errors and dimensions
A Moosavi, R Ştefănescu… - International Journal for …, 2018 - Wiley Online Library
This paper introduces multivariate input‐output models to predict the errors and bases
dimensions of local parametric Proper Orthogonal Decomposition reduced‐order models …
dimensions of local parametric Proper Orthogonal Decomposition reduced‐order models …
Regularized reduced order models for a stochastic Burgers equation
In this paper, we study the numerical stability of reduced order models for convection-
dominated stochastic systems in a relatively simple setting: a stochastic Burgers equation …
dominated stochastic systems in a relatively simple setting: a stochastic Burgers equation …
Efficient construction of local parametric reduced order models using machine learning techniques
A Moosavi, R Stefanescu, A Sandu - arXiv preprint arXiv:1511.02909, 2015 - arxiv.org
Reduced order models are computationally inexpensive approximations that capture the
important dynamical characteristics of large, high-fidelity computer models of physical …
important dynamical characteristics of large, high-fidelity computer models of physical …
A goal-oriented adaptive discrete empirical interpolation method
R Stefanescu, A Sandu - arXiv preprint arXiv:1901.05343, 2019 - arxiv.org
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 …
loss in quantities of interests computed using reduced order models. To generate the …
Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification
AS Zavar Moosavi - 2018 - vtechworks.lib.vt.edu
Simulations and modeling of large-scale systems are vital to understanding real world
phenomena. However, even advanced numerical models can only approximate the true …
phenomena. However, even advanced numerical models can only approximate the true …
[PDF][PDF] Computer Science Technical Report CSTR-22 November 12, 2015
A Moosavi, R Stefanescu, A Sandu - arXiv preprint arXiv …, 2015 - researchgate.net
Reduced order models are computationally inexpensive approximations that capture the
important dynamical characteristics of large, high-fidelity computer models of physical …
important dynamical characteristics of large, high-fidelity computer models of physical …
[引用][C] Multivariate Predictions of Local Parametric Reduced-Order Models Characteristics and Their Applications
A Moosavi, R Stefanescub, A Sandua