Name
Title | ||
|---|---|---|
From Mesoscale Back to Microscale: Reconstruction Schemes for Coarse-Grained Stochastic Lattice Systems |
Abstract | ||
|---|---|---|
Starting from a microscopic stochastic lattice spin system and the corresponding coarse-grained model we introduce a mathematical strategy to recover microscopic information given the coarse-grained data. We define “reconstructed" microscopic measures satisfying two conditions: (i) they are close in specific relative entropy to the initial microscopic equilibrium measure conditioned on the coarse-grained, data, and (ii) their sampling is computationally advantageous when compared to sampling directly from the conditioned microscopic equilibrium measure. By using different techniques we consider the cases of both short and long range microscopic models. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1137/080722382 | SIAM J. Numerical Analysis |
Keywords | Field | DocType |
long range,microscopic measure,different technique,lattice spin systems,monte-carlo simulation,gibbs measure,microscopic information,cluster expansion.,parallel computing,coarse-grained data,microscopic reconstruction,coarse-graining,microscopic stochastic lattice spin,reconstruction schemes,microscopic model,initial microscopic equilibrium measure,microscopic equilibrium measure,coarse-grained stochastic lattice systems,corresponding coarse-grained model,monte carlo simulation,relative entropy,satisfiability,coarse graining,parallel computer | Gibbs measure,Statistical physics,Monte Carlo method,Microscale chemistry,Initial value problem,Sampling (statistics),Granularity,Numerical analysis,Mathematics,Kullback–Leibler divergence | Journal |
Volume | Issue | ISSN |
48 | 5 | 0036-1429 |
Citations | PageRank | References |
5 | 0.89 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| José Trashorras | 1 | 5 | 0.89 |
| Dimitrios K. Tsagkarogiannis | 2 | 5 | 0.89 |