Bernstein polynomials: a bibliometric data analysis since the year 1949 based on the Scopus database
February 21, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Rushan Ziatdinov
arXiv ID
2503.07614
Category
math.HO
Cross-listed
cs.DL,
cs.GR
Citations
0
Venue
arXiv.org
Last Checked
1 month ago
Abstract
It's hard to imagine human life in the digital and AI age without polynomials because they are everywhere but mostly invisible to ordinary people: in data trends, on computer screens, in the shapes around us, and in the very fabric of technology. One of these, the simple but elegant Bernstein polynomials, was discovered by a scientist from the Russian Empire, Sergei Bernstein, in 1912 and plays a central role in mathematical analysis, computational and applied mathematics, geometric modelling, computer-aided geometric design, computer graphics and other areas of science and engineering. They have been the sub-ject of much research for over a hundred years. However, no work has carried out database-derived research analysis, such as bibliometric, keyword or network analysis, or more generally, data analysis of manuscript data related to Bernstein polynomials extracted from digital academic databases. This work, which appears to be the first-ever attempt at the bibliometric data analysis of Bernstein polynomials, aims to fill this gap and open researchers' eyes to potentially new or underexplored areas of mathematics and engineering where Bernstein polynomials may one day be used to make discoveries. The results may be helpful to academics researching Bernstein polynomials and looking for potential applications, collaborators, supervisors, funding or journals to publish in.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β math.HO
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Quantum GestART: Identifying and Applying Correlations between Mathematics, Art, and Perceptual Organization
R.I.P.
π»
Ghosted
The Mathematical Intelligencer flunks the Olympics
R.I.P.
π»
Ghosted
Non-Euclidean Virtual Reality IV: Sol
R.I.P.
π»
Ghosted
Elitism in Mathematics and Inequality
R.I.P.
π»
Ghosted
From Good to Great: Improving Math Reasoning with Tool-Augmented Interleaf Prompting
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted