Machine Learning is Good for Physics - and Vice Versa

2026/08/06 by Michael Krämer, Tilman Plehn
Physics and Astronomy · #hep-ph

paper · pdf

10 pages

arxiv created 2026/08/06 · arxiv updated 2026/08/07

Abstract

Scientific AI is rapidly transforming fundamental physics research and challenging defining aspects of the fundamental physics methodology. We discuss opportunities and dangers of this transformation and find exciting benefits from a close interaction between AI and fundamental physics, provided that we remain aware of the scientific methodologies of the respective fields. For fundamental physics, we discuss two such aspects: statistical validation and a generalizing theory description, both with the goal of discovering new physics in vast datasets.

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