Problem reduction, renormalization, and memory
Numerical Analysis
2007-05-23 v1
Abstract
Methods for the reduction of the complexity of computational problems are presented, as well as their connections to renormalization, scaling, and irreversible statistical mechanics. Several statistically stationary cases are analyzed; for time dependent problems averaging usually fails, and averaged equations must be augmented by appropriate memory and random forcing terms. Approximations are described and examples are given.
Cite
@article{arxiv.math/0503612,
title = {Problem reduction, renormalization, and memory},
author = {Alexandre J. Chorin and Panagiotis Stinis},
journal= {arXiv preprint arXiv:math/0503612},
year = {2007}
}