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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.04161 |
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| _version_ | 1866915257462554624 |
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| author | Vinuesa, Ricardo Cinnella, Paola Rabault, Jean Azizpour, Hossein Bauer, Stefan Brunton, Bingni W. Elofsson, Arne Jarlebring, Elias Kjellstrom, Hedvig Markidis, Stefano Marlevi, David Garcia-Martinez, Javier Brunton, Steven L. |
| author_facet | Vinuesa, Ricardo Cinnella, Paola Rabault, Jean Azizpour, Hossein Bauer, Stefan Brunton, Bingni W. Elofsson, Arne Jarlebring, Elias Kjellstrom, Hedvig Markidis, Stefano Marlevi, David Garcia-Martinez, Javier Brunton, Steven L. |
| contents | As modern scientific instruments generate vast amounts of data and the volume of information in the scientific literature continues to grow, machine learning (ML) has become an essential tool for organising, analysing, and interpreting these complex datasets. This paper explores the transformative role of ML in accelerating breakthroughs across a range of scientific disciplines. By presenting key examples -- such as brain mapping and exoplanet detection -- we demonstrate how ML is reshaping scientific research. We also explore different scenarios where different levels of knowledge of the underlying phenomenon are available, identifying strategies to overcome limitations and unlock the full potential of ML. Despite its advances, the growing reliance on ML poses challenges for research applications and rigorous validation of discoveries. We argue that even with these challenges, ML is poised to disrupt traditional methodologies and advance the boundaries of knowledge by enabling researchers to tackle increasingly complex problems. Thus, the scientific community can move beyond the necessary traditional oversimplifications to embrace the full complexity of natural systems, ultimately paving the way for interdisciplinary breakthroughs and innovative solutions to humanity's most pressing challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04161 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Decoding complexity: how machine learning is redefining scientific discovery Vinuesa, Ricardo Cinnella, Paola Rabault, Jean Azizpour, Hossein Bauer, Stefan Brunton, Bingni W. Elofsson, Arne Jarlebring, Elias Kjellstrom, Hedvig Markidis, Stefano Marlevi, David Garcia-Martinez, Javier Brunton, Steven L. Machine Learning Artificial Intelligence As modern scientific instruments generate vast amounts of data and the volume of information in the scientific literature continues to grow, machine learning (ML) has become an essential tool for organising, analysing, and interpreting these complex datasets. This paper explores the transformative role of ML in accelerating breakthroughs across a range of scientific disciplines. By presenting key examples -- such as brain mapping and exoplanet detection -- we demonstrate how ML is reshaping scientific research. We also explore different scenarios where different levels of knowledge of the underlying phenomenon are available, identifying strategies to overcome limitations and unlock the full potential of ML. Despite its advances, the growing reliance on ML poses challenges for research applications and rigorous validation of discoveries. We argue that even with these challenges, ML is poised to disrupt traditional methodologies and advance the boundaries of knowledge by enabling researchers to tackle increasingly complex problems. Thus, the scientific community can move beyond the necessary traditional oversimplifications to embrace the full complexity of natural systems, ultimately paving the way for interdisciplinary breakthroughs and innovative solutions to humanity's most pressing challenges. |
| title | Decoding complexity: how machine learning is redefining scientific discovery |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2405.04161 |