Problem reduction, renormalization, and memory

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.

References in corpus (3)

• A comparative study of two stochastic mode reduction methods
• Mode coupling theory for multi-point and multi-time correlation functions
• A maximum likelihood algorithm for the estimation and renormalization of exponential densities

Cited by in corpus (4)

• Data-driven non-Markovian closure models
• Application of the t-model of optimal prediction to the estimation of the rate of decay of solutions of the Euler equations in two and three dimensions
• Computation of the Memory Functions in the Generalized Langevin Models for Collective Dynamics of Macromolecules
• General tooth boundary conditions for equation free modelling