Problem reduction, renormalization, and memory
数值分析
2007-05-23 v1
摘要
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.
引用
@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}
}