The State of Julia for Scientific Machine Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913620333428736 |
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| author | Berman, Edward Ginesin, Jacob |
| author_facet | Berman, Edward Ginesin, Jacob |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_10908 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The State of Julia for Scientific Machine Learning Berman, Edward Ginesin, Jacob Machine Learning Mathematical Software Programming Languages 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. |
| title | The State of Julia for Scientific Machine Learning |
| topic | Machine Learning Mathematical Software Programming Languages |
| url | https://arxiv.org/abs/2410.10908 |