4 papers
JetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
Guillaume Letellier, Antonin Vacheret, Frédéric Jurie
Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustne…
Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers
Giorgio Morales, Gregory Lehaut, Antonin Vacheret +2
Neutrino oscillations encode fundamental information about neutrino masses and mixing parameters, offering a unique window into physics beyond the Standard Model. Estimating these…
Towards Uncertainty Quantification in Generative Model Learning
Giorgio Morales, Frederic Jurie, Jalal Fadili
While generative models have become increasingly prevalent across various domains, fundamental concerns regarding their reliability persist. A crucial yet understudied aspect of th…
SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problems
Sidney Besnard, Frédéric Jurie, Jalal M. Fadili
This paper introduces a novel approach to solve inverse problems by leveraging deep learning techniques. The objective is to infer unknown parameters that govern a physical system…