ELGIN is a graph-based physics-informed surrogate model that predicts carrier flow and polydisperse particle motion in dental aerosol scenarios, achieving lower tracking errors and 37x speedup versus full OpenFOAM CFD in a preliminary single-case test.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Regression analysis of 53k astrophysics papers finds open data, open access, and open code each linked to higher citation counts after controlling for grants, authors, length, date, and subfield.
citing papers explorer
-
Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
ELGIN is a graph-based physics-informed surrogate model that predicts carrier flow and polydisperse particle motion in dental aerosol scenarios, achieving lower tracking errors and 37x speedup versus full OpenFOAM CFD in a preliminary single-case test.
-
Open Science in Astrophysics: Citation Benefits of Open Code, Open Data, and Open Access
Regression analysis of 53k astrophysics papers finds open data, open access, and open code each linked to higher citation counts after controlling for grants, authors, length, date, and subfield.