The State of Julia for Scientific Machine Learning
Machine Learning
2024-12-23 v2 Mathematical Software
Programming Languages
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.
Keywords
Cite
@article{arxiv.2410.10908,
title = {The State of Julia for Scientific Machine Learning},
author = {Edward Berman and Jacob Ginesin},
journal= {arXiv preprint arXiv:2410.10908},
year = {2024}
}
Comments
Presented at the 2024 NeurIPS Machine Learning and the Physical Sciences Workshop