mlsanityData to 14 Oct 2026

Outlook Towards Deployable Continual Learning for Particle Accelerators

arXiv 2504.03793 ↗First posted 4 Apr 2025cs.LG cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization and control, anomaly detection and machine protection. With recent advancements, Machine Learning (ML) holds promise to assist in more advance prognostics, optimization, and control. While ML based solutions have been developed for several applications in particle accelerators, only few have reached deployment and even fewer to long term usage, due to particle accelerator data distribution drifts caused by changes in both measurable and non-measurable parameters. In this paper, we identify some of the key areas within particle accelerators where continual learning can allow maintenance of ML model performance with distribution drifts. Particularly, we first discuss existing applications of ML in particle accelerators, and their limitations due to distribution drift. Next, we review existing continual learning techniques and investigate their potential applications to address data distribution drifts in accelerators. By identifying the opportunities and challenges in applying continual learning, this paper seeks to open up the new field and inspire more research efforts towards deployable continual learning for particle accelerators.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 39 of its 116 references that are in the map. First posted on arXiv 4 Apr 2025.

Builds on

39 of 116 references.

References · 39

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  4. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  5. 2016Matching Networks for One Shot Learningcited by 2,255
  6. 2017Gradient Episodic Memory for Continual Learningcited by 1,898
  7. 2017Continual Learning with Deep Generative Replaycited by 1,158
  8. 2017Hindsight Experience Replaycited by 1,136
  9. 2017Memory Aware Synapses: Learning what (not) to forgetcited by 1,105
  10. 2015Prioritized Experience Replaycited by 1,099
  11. 2018Experience Replay for Continual Learningcited by 1,095
  12. 2018Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigencecited by 742
27 more
  1. 2016Learning to learn by gradient descent by gradient descentcited by 620
  2. 2019Three scenarios for continual learningcited by 614
  3. 2018Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interferencecited by 521
  4. 2018Progress & Compress: A scalable framework for continual learningcited by 520
  5. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 461
  6. 2015Deep Kernel Learningcited by 445
  7. 2020Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awarenesscited by 389
  8. 2021Gradient Projection Memory for Continual Learningcited by 348
  9. 2018Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learningcited by 276
  10. 2020Revisiting Fundamentals of Experience Replaycited by 175
  11. 2020Continual Lifelong Learning in Natural Language Processing: A Surveycited by 152
  12. 2019Online Meta-Learningcited by 137
  13. 2020Bayesian Neural Networks: An Introduction and Surveycited by 131
  14. 2018Memory Efficient Experience Replay for Streaming Learningcited by 128
  15. 2017Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learningcited by 126
  16. 2019Recurrent Neural Networks (RNNs): A gentle Introduction and Overviewcited by 68
  17. 2022Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfercited by 59
  18. 2023Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learningcited by 53
  19. 2020Meta-Gradient Reinforcement Learning with an Objective Discovered Onlinecited by 52
  20. 2023Continual Learning on Dynamic Graphs via Parameter Isolationcited by 36
  21. 2017McDiarmid Drift Detection Methods for Evolving Data Streamscited by 13
  22. 2020Gradient Episodic Memory with a Soft Constraint for Continual Learningcited by 12
  23. 2020Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FELcited by 8
  24. 2020Autonomous Control of a Particle Accelerator using Deep Reinforcement Learningcited by 7
  25. 2021Developing Robust Digital Twins and Reinforcement Learning for Accelerator Control Systems at the Fermilab Boostercited by 6
  26. 2024Uncertainty-Aware Out-of-Distribution Detection with Gaussian Processescited by 6
  27. 2023Algorithm Design for Online Meta-Learning with Task Boundary Detectioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Toward a Fully Autonomous, AI-Native Particle Acceleratorcited by 0