The rise of machine learning is moving research away from tightly controlled, theory-guided experiments towards an approach based on data-driven searches. Abbas Ourmazd describes how this change might profoundly affect our understanding and practice of physics.
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Acknowledgements
The author acknowledges valuable discussions with Tony Hey, Larry Jackel, E. Lattman and many UWM colleagues. Any errors are the sole responsibility of the author. This work was supported by the US Department of Energy, Office of Science, Basic Energy Sciences under award DE-SC0002164 (algorithm design and development), and by the US National Science Foundation under awards STC 1231306 (numerical trial models and data analysis) and 1551489 (underlying analytical models).
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Ourmazd, A. Science in the age of machine learning. Nat Rev Phys 2, 342–343 (2020). https://doi.org/10.1038/s42254-020-0191-7
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DOI: https://doi.org/10.1038/s42254-020-0191-7
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