Top-down Coarse-graining
Top-Down Coarse-Graining is a coarse-graining methodology that belongs to the class of modeling approaches which derive simplified representations by enforcing consistency with macroscopic observables or experimental data, distinguished by its imposition of global constraints to define effective interactions without explicit reference to underlying microscopic details.
10
Publications
575
Citations
32
Authors
25
Institutions
Publications per year
2017–2026
6
Parents
10
1–5 of 10
32
2
h-index
University of Massachusetts Amherst, United States
2 publications · 27 citations
2
h-index
1
h-index
1
h-index
National Institute of Standards and Technology, United States
1 publications · 87 citations
1
h-index
1–5 of 32
25

University of Massachusetts Amherst
Amherst Center, United States
2 h-index
3 publications
42 citations
Purdue University West Lafayette
West Lafayette, United States
1 h-index
2 publications
48 citations
University of Tennessee at Knoxville
Knoxville, United States
1 h-index
1 publications
15 citations

Durham Technical Community College
Durham, United States
1 h-index
3 publications
177 citations
Foundation for Research and Technology Hellas
Heraklion, Greece
1 h-index
1 publications
12 citations
Clemson University
Clemson, United States
1 h-index
1 publications
26 citations
Universitat de Barcelona
Barcelona, Spain
1 h-index
1 publications
144 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
1 h-index
3 publications
423 citations
1–8 of 25
2 publications · 53 citations
2
h-index
1 publications · 26 citations
1
h-index
1 publications · 87 citations
1
h-index
PRE · 1 publications · 144 citations
1
h-index
1 publications · 24 citations
1
h-index
1–5