Content area

Abstract

Julia has been heralded as a potential successor to Python for scientific machine learning and numerical computing, boasting ergonomic and performance improvements. Since Julia's inception in 2012 and declaration of language goals in 2017, its ecosystem and language-level features have grown tremendously. In this paper, we take a modern look at Julia's features and ecosystem, assess the current state of the language, and discuss its viability and pitfalls as a replacement for Python as the de-facto scientific machine learning language. We call for the community to address Julia's language-level issues that are preventing further adoption.

Details

1009240
Title
The State of Julia for Scientific Machine Learning
Publication title
arXiv.org; Ithaca
Publication year
2024
Publication date
Dec 20, 2024
Section
Computer Science
Publisher
Cornell University Library, arXiv.org
Source
arXiv.org
Place of publication
Ithaca
Country of publication
United States
University/institution
Cornell University Library arXiv.org
e-ISSN
2331-8422
Source type
Working Paper
Language of publication
English
Document type
Working Paper
Publication history
 
 
Online publication date
2024-12-23
Milestone dates
2024-10-14 (Submission v1); 2024-12-20 (Submission v2)
Publication history
 
 
   First posting date
23 Dec 2024
ProQuest document ID
3117168867
Document URL
https://www.proquest.com/working-papers/state-julia-scientific-machine-learning/docview/3117168867/se-2?accountid=208611
Full text outside of ProQuest
Copyright
© 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2024-12-24
Database
ProQuest One Academic