Continual Learning for Particle Accelerators
Unfortunately, I could not travel to the conference, thanks to Malachi Schram for presenting this spotlight Talk on our paper "Continual Learning for Particle Accelerators"
admin
•
December 6, 2025 12:00 AM — 12:00 AM
•
1 min read
Continual learning for particle accelerators
This paper addresses the challenge of continual learning in particle accelerators, where machine learning models must adapt to changing data distributions and system configurations …
rajput-k.
Hydra: An AI-Based Framework for Interpretable and Portable Data Quality Monitoring
Hydra is an advanced framework designed for training and managing AI models for near real time data quality monitoring at Jefferson Lab. Deployed in all four experimental halls, …
britton-t.
Towards continual machine learning for particle accelerators
This paper presents results from ongoing efforts to develop continual learning methods for particle accelerators. We address the challenge of model adaptation to changing …
rajput-k.
Distance preserving machine learning for uncertainty aware accelerator capacitance predictions
This paper presents a distance-preserving machine learning approach for making uncertainty-aware predictions of accelerator capacitance. The method preserves geometric …
goldenberg-s.
Uncertainty aware deep learning for particle accelerators
Deep learning models for particle accelerator control and optimization can suffer from overconfidence when deployed to new operating conditions. This paper presents techniques for …
rajput-k.
Multi-module-based CVAE to predict HVCM faults in the SNS accelerator
This paper presents a conditional variational autoencoder (CVAE) approach for predicting high-voltage converter module (HVCM) faults in the SNS accelerator. The multi-module …
alanazi-y.
Uncertainty aware machine-learning-based surrogate models for particle accelerators: Study at the Fermilab Booster Accelerator Complex
Surrogate models based on machine learning are increasingly used to accelerate optimization and control of particle accelerators. This paper presents methods for quantifying …
schram-m.