“VERaiPHY — Validation & Evaluation for Robust AI in PHYsics” Paper Series

  • [arXiv:2608.17724]: VERaiPHY — Validation & Evaluation for Robust AI in PHYsics: Overview
  • [arXiv:2606.20299]: Statistical Properties of Training & Generalization
  • [arXiv:2605.10378]: Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
  • [arXiv:2608.13633]: Unknown Unknowns: Model Misspecification in Machine Learning for Physics
  • [arXiv:2607.21702]: An Introduction to Bayesian and Frequentist Simulation-Based Inference with Machine Learning
  • [arXiv:2605.30453]: Generative Models and Statistical Validation
  • [arXiv:2605.31103]: Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice
  • [in preparation]: Symmetry-Informed Machine Learning for Fundamental Physics
  • [arXiv:2606.26228]: Interpreting “Interpretability” and Explaining “Explainability” in Machine Learning in Physics
  • [in preparation]: Representation Learning in Fundamental Physics
  • [arXiv:2607.10039]: Are We Ready for AI-Driven Discovery? AI Verification Before the Next Fundamental Physics Breakthrough

Machine Learning for Particle Physics (ML4Jets) - Workshop Series

Higgs Effective Field Theories (HEFT) - Workshop Series

Experiments