UQ4ML | COMETA Workshop on Uncertainty Quantification for Machine Learning

Session

Deep Learning and Uncertainty Quantification

UQ4ML/20250917/dl-uq

Sep 17, 2025, 2:00 PM

Amphithéâtre Claude Bloch (IPhT) (CEA Paris-Saclay)

Conveners

Deep Learning and Uncertainty Quantification: Keynote

• Alessandra Cappati (Universite Catholique de Louvain (UCL) (BE))

Deep Learning and Uncertainty Quantification: Talks

• Alessandra Cappati (Universite Catholique de Louvain (UCL) (BE))

48. Uncertainty Quantification for Neural Networks in Particle Physics

Anja Butter (Centre National de la Recherche Scientifique (FR))

9/17/25, 2:00 PM

Deep Learning and Uncertainty Quantification

Keynote

Correctly calibrated uncertainties have always been a fundamental pillar of particle physics. As machine learning becomes increasingly integrated into both experimental and theoretical workflows, it is essential that neural network predictions include robust and reliable uncertainty estimates.

This talk will review current approaches to uncertainty estimation in neural networks, focusing on...

34. Uncertainty Quantification in an ML Pattern Recognition Pipeline

Lukas Péron

9/17/25, 3:30 PM

Deep Learning and Uncertainty Quantification

Short-talk

Geometric learning pipelines have achieved state-of-the-art performance in High-Energy and Nuclear Physics reconstruction tasks like flavor tagging and particle tracking [1]. Starting from a point cloud of detector or particle-level measurements, a graph can be built where the measurements are nodes, and where the edges represent all possible physics relationships between the nodes. Depending...

44. Uncertainty quantification for machine learning: a new approach for the Critical Heat Flux application

Michele Cazzola (CEA Paris-Saclay)

9/17/25, 4:00 PM

Deep Learning and Uncertainty Quantification

Short-talk

Critical Heat Flux (CHF) represents a concern for the nuclear safety, as it leads to a rapid drop down in the heat transfer between a heated surface and the liquid coolant in the core of nuclear reactors. This could cause several issues to the system, including structural damage and release of radioactive material.

The main challenge related to CHF prediction is the highly non-linear...