Projective and Coarse Projective Integration for Problems with Continuous Symmetries

Abstract

Temporal integration of equations possessing continuous symmetries (e.g. systems with translational invariance associated with traveling solutions and scale invariance associated with self-similar solutions) in a ``co-evolving'' frame (i.e. a frame which is co-traveling, co-collapsing or co-exploding with the evolving solution) leads to improved accuracy because of the smaller time derivative in the new spatial frame. The slower time behavior permits the use of {\it projective} and {\it coarse projective} integration with longer projective steps in the computation of the time evolution of partial differential equations and multiscale systems, respectively. These methods are also demonstrated to be effective for systems which only approximately or asymptotically possess continuous symmetries. The ideas of projective integration in a co-evolving frame are illustrated on the one-dimensional, translationally invariant Nagumo partial differential equation (PDE). A corresponding kinetic Monte Carlo model, motivated from the Nagumo kinetics, is used to illustrate the coarse-grained method. A simple, one-dimensional diffusion problem is used to illustrate the scale invariant case. The efficiency of projective integration in the co-evolving frame for both the macroscopic diffusion PDE and for a random-walker particle based model is again demonstrated.

References in corpus (7)

• Coarse Projective kMC Integration: Forward/Reverse Initial and Boundary Value Problems
• An equation-free computational approach for extracting population-level behavior from individual-based models of biological dispersal
• Numerical Renormalization Group Calculations For Similarity Solutions and Travelling Waves
• Core Collapse via Coarse Dynamic Renormalization
• Going with the Flow: a Lagrangian approach to self-similar dynamics and its consequences
• Equation-free Dynamic Renormalization of a KPZ-type Equation
• Effective equations for discrete systems: A time stepper based approach

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