Coarse-graining schemes and a posteriori error estimates for stochastic lattice systems
Department of Mathematics, University of Massachusetts, USA.
2 Department of Mathematics, University of Tennessee, USA. email@example.com
3 Max Planck Institute for Mathematics in the Sciences, Germany. firstname.lastname@example.org
Revised: 1 February 2007
The primary objective of this work is to develop coarse-graining schemes for stochastic many-body microscopic models and quantify their effectiveness in terms of a priori and a posteriori error analysis. In this paper we focus on stochastic lattice systems of interacting particles at equilibrium. The proposed algorithms are derived from an initial coarse-grained approximation that is directly computable by Monte Carlo simulations, and the corresponding numerical error is calculated using the specific relative entropy between the exact and approximate coarse-grained equilibrium measures. Subsequently we carry out a cluster expansion around this first – and often inadequate – approximation and obtain more accurate coarse-graining schemes. The cluster expansions yield also sharp a posteriori error estimates for the coarse-grained approximations that can be used for the construction of adaptive coarse-graining methods. We present a number of numerical examples that demonstrate that the coarse-graining schemes developed here allow for accurate predictions of critical behavior and hysteresis in systems with intermediate and long-range interactions. We also present examples where they substantially improve predictions of earlier coarse-graining schemes for short-range interactions.
Mathematics Subject Classification: 65C05 / 65C20 / 82B20 / 82B80 / 82-08
Key words: Coarse-graining / a posteriori error estimate / relative entropy / lattice spin systems / Monte Carlo method / Gibbs measure / cluster expansion / renormalization group map.
© EDP Sciences, SMAI, 2007