Machine learning is good for physics—and vice versa

The European Physical Journal C, vol. 86

Abstract

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.

Authors 2

1. Michael Krämer Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Tilman Plehn corresponding

   Heidelberg University

   Affiliation as printed

   Universität Heidelberg