Publication:
Numerical and statistical methods for the coarse-graining of many-particle stochastic systems

Simple item page
dc.contributor.authorKatsoulakis, MA
dc.contributor.authorPlechac, P
dc.contributor.authorRey-Bellet, L
dc.contributor.departmentUniversity of Massachusetts - Amherst
dc.date2023-09-23T01:05:25.000
dc.date.accessioned2024-04-26T18:41:25Z
dc.date.available2024-04-26T18:41:25Z
dc.date.issued2008-01-01
dc.description<p>The published version is located at <a href="http://www.springerlink.com/content/u12351v031471759/">http://www.springerlink.com/content/u12351v031471759/</a></p>
dc.description.abstractIn this article we discuss recent work on coarse-graining methods for microscopic stochastic lattice systems. We emphasize the numerical analysis of the schemes, focusing on error quantification as well as on the construction of improved algorithms capable of operating in wider parameter regimes. We also discuss adaptive coarse-graining schemes which have the capacity of automatically adjusting during the simulation if substantial deviations are detected in a suitable error indicator. The methods employed in the development and the analysis of the algorithms rely on a combination of statistical mechanics methods (renormalization and cluster expansions), statistical tools (reconstruction and importance sampling) and PDE-inspired analysis (a posteriori estimates). We also discuss the connections and extensions of our work on lattice systems to the coarse-graining of polymers.
dc.description.pages43-71
dc.identifier.urihttps://hdl.handle.net/20.500.14394/34686
dc.relation.ispartofJOURNAL OF SCIENTIFIC COMPUTING
dc.source.issue1
dc.source.issue37
dc.source.statuspublished
dc.subjectcoarse-graining
dc.subjectrelative entropy
dc.subjectlattice spin systems
dc.subjectpolymeric systems
dc.subjectMonte Carlo method
dc.subjectgibbs measure
dc.subjectcluster expansion
dc.subjectmulti-body interactions
dc.subjectrenormalization group map
dc.subjectadaptivity
dc.subjecta posteriori error analysis
dc.subjectimportance sampling
dc.subjectPhysical Sciences and Mathematics
dc.titleNumerical and statistical methods for the coarse-graining of many-particle stochastic systems
dc.typearticle
dc.typearticle
digcom.contributor.authorisAuthorOfPublication|email:markos@math.umass.edu|institution:University of Massachusetts - Amherst|Katsoulakis, MA
digcom.contributor.authorPlechac, P
digcom.contributor.authorRey-Bellet, L
digcom.identifiermath_faculty_pubs/439
digcom.identifier.contextkey1687801
digcom.identifier.submissionpathmath_faculty_pubs/439
dspace.entity.typePublication
relation.isAuthorOfPublication15dace41-ff9d-4423-bf6e-02ac6d133216
relation.isAuthorOfPublication.latestForDiscovery15dace41-ff9d-4423-bf6e-02ac6d133216
Files
Collections
Mathematics and Statistics Department Faculty Publication Series