Decoding complexity through machine learning is redefining scientific discovery
Abstract
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.
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BibTeX
@article{vinuesa2026decoding,
title = {{Decoding complexity through machine learning is redefining scientific discovery}},
author = {Ricardo Vinuesa and Paola Cinnella and Jean Rabault and Hossein Azizpour and Stefan Bauer and Bingni Brunton and Arne Elofsson and Elias Jarlebring and Hedvig Kjellström and Stefano Markidis and David Marlevi and Javier García-Martínez and Steven Brunton},
journal = {Communications Physics},
year = {2026},
month = may,
doi = {10.1038/s42005-026-02676-7},
url = {https://hal.science/hal-05733584v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - Decoding complexity through machine learning is redefining scientific discovery
AU - Ricardo Vinuesa
AU - Paola Cinnella
AU - Jean Rabault
AU - Hossein Azizpour
AU - Stefan Bauer
AU - Bingni Brunton
AU - Arne Elofsson
AU - Elias Jarlebring
AU - Hedvig Kjellström
AU - Stefano Markidis
AU - David Marlevi
AU - Javier García-Martínez
AU - Steven Brunton
PY - 2026
DA - 2026/05/14
JO - Communications Physics
DO - 10.1038/s42005-026-02676-7
UR - https://hal.science/hal-05733584v1
AB - 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.
SN - 2399-3650
SN - 2399-3650
ER -