Author

T. Plehn

2 papers indexed here

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Preprint Aug 2026

VERaiPHY -- Validation&Evaluation for Robust AI in PHYsics

Modern machine learning is leading to substantial gains in precision, flexibility, and computational efficiency in fundamental physics. Statistical validation, uncertainty quantification, and robustness assessment are less systematically addressed. The VERaiPHY initiative (Validation&Evaluation for Robust AI in PHYsics...

Gaia Grosso, Ramon Winterhalder, Lydia Brenner et al. · 2 citations

Open access Aug 2026

Machine learning is good for physics—and vice versa

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...

Michael Krämer, T. Plehn · The European Physical Journa... · 2 citations