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<p>As scientific instruments and the literature generate ever larger volumes of data, machine learning (ML) has become essential for organizing, analyzing and interpreting complex information. This Perspective examines how ML accelerates discovery across disciplines, with examples such as brain mapping and exoplanet detection. It also considers situations with different levels of prior knowledge about the underlying phenomenon, outlining strategies to address limitations and exploit ML effectively. Although growing reliance on ML raises challenges for research practice and validation, it is reshaping scientific methods and expanding what can be studied. We also highlight foundation models as a promising route to faster, broader scientific discovery.</p>

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Decoding complexity through machine learning is redefining scientific discovery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月14日

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  • Ricardo Vinuesa,
  • Paola Cinnella,
  • Jean Rabault,
  • Hossein Azizpour,
  • Stefan Bauer,
  • Bingni W. Brunton,
  • Arne Elofsson,
  • Elias Jarlebring,
  • Hedvig Kjellström,
  • Stefano Markidis,
  • David Marlevi,
  • Javier García-Martínez,
  • Steven L. Brunton

摘要

As scientific instruments and the literature generate ever larger volumes of data, machine learning (ML) has become essential for organizing, analyzing and interpreting complex information. This Perspective examines how ML accelerates discovery across disciplines, with examples such as brain mapping and exoplanet detection. It also considers situations with different levels of prior knowledge about the underlying phenomenon, outlining strategies to address limitations and exploit ML effectively. Although growing reliance on ML raises challenges for research practice and validation, it is reshaping scientific methods and expanding what can be studied. We also highlight foundation models as a promising route to faster, broader scientific discovery.