Conveners
5.2 Physics-informed AI & Integration of physics and ML
- Maurizio Pierini (CERN)
Gauge symmetry is fundamental to describing quantum chromodynamics on a lattice. While the local nature of gauge symmetry presents challenges for machine learning due to the vast and intricate parameter space, which involves distinct group transformations at each spacetime point, it remains a fundamental and indispensable prior in physics. Lattice gauge equivariant convolutional neural...
In recent years, deep learning algorithms have excelled in various domains, including Astronomy. Despite this success, few deep learning models are planned for online deployment in the O4 data collection run of the LIGO-Virgo-KAGRA collaboration. This is partly due to a lack of standardized software tools for quick implementation and deployment of novel ideas with confidence in production...
The Multi-disciplinary Use Cases for Convergent new Approaches to AI explainability (MUCCA) project is pioneering efforts to enhance the transparency and interpretability of AI algorithms in complex scientific endeavours. The presented study focuses on the role of Explainable AI (xAI) in the domain of high-energy physics (HEP). Approaches based on Machine Learning (ML) methodologies, from...
