@Article{IJNAM-15-4-5,
author = {Max, Gunzburger and Nan, Jiang and Schneier, Michael},
title = {A Higher-Order Ensemble/Proper Orthogonal Decomposition Method for the Nonstationary Navier-Stokes Equations},
journal = {International Journal of Numerical Analysis and Modeling},
year = {2018},
volume = {15},
number = {4-5},
pages = {608--627},
abstract = {<p style="text-align: justify;">Partial differential equations (PDE) often involve parameters, such as viscosity or
density. An analysis of the PDE may involve considering a large range of parameter values, as
occurs in uncertainty quantification, control and optimization, inference, and several statistical
techniques. The solution for even a single case may be quite expensive; whereas parallel computing
may be applied, this reduces the total elapsed time but not the total computational effort. In the
case of flows governed by the Navier-Stokes equations, a method has been devised for computing
an ensemble of solutions. Recently, a reduced-order model derived from a proper orthogonal
decomposition (POD) approach was incorporated into a first-order accurate in time version of the
ensemble algorithm. In this work, we expand on that work by incorporating the POD reduced
order model into a second-order accurate ensemble algorithm. Stability and convergence results
for this method are updated to account for the POD/ROM approach. Numerical experiments
illustrate the accuracy and efficiency of the new approach.</p>},
issn = {2617-8710},
doi = {https://doi.org/2018-IJNAM-12534},
url = {https://global-sci.com/article/83131/a-higher-order-ensembleproper-orthogonal-decomposition-method-for-the-nonstationary-navier-stokes-equations}
}