Differential and Explainable Reinforcement Learning for Multi-objective Optimization in Particle Accelerators
Invited Talk Explainable and Differential Reinforcement Learning for Multi-objective Optimization in Particle Accelerators
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October 27, 2025 11:00 AM — October 29, 2025 11:15 AM
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Towards continual machine learning for your ever-changing accelerators
Invited Talk Continual/Lifelong machine learning for ever drifting particle accelerator data at IBIC 2025, University of Liverpool, UK.
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An Outlook Towards Deployable Continual Learning for Particle Accelerators
Particle accelerators rely on the precise synchronization of thousands of components, but data distribution drifts often limit the long-term deployment of machine learning …
rajput-k.
Harnessing the Power of Gradient Based Simulations for Multi-Objective Optimization in Particle Accelerators
Invited Talk Talk on applying fast optimization and control with differential reinforcement learning leveraging differential simulations.
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Explainable physics-based constraints on reinforcement learning for accelerator controls
By examining the mathematical form of the learned constraint function, we are able to confirm the agent has learned to use the established physics of each environment. In addition, …
colen-j.
Errant Beam Prognostics with Machine Leaning at SNS Accelerator
Invited Talk Talk on application of conditional machine learning models to predict anomalies before they occur at SNS accelerator.
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Machine Learning for Prognostics and Optimization of Particle Accelerators
Invited Talk Current progress on machine learning applications for particle accelerators and its scope for Fusion Science.
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Fault Prediction in Particle Accelerators
Guest Lecture Guest lecture within the graduate particle accelerators class at ODU department of physics.
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Robust errant beam prognostics with conditional modeling for particle accelerators
Conditional Models to handle data drifts due to changes in beam configuration changes at SNS accelerator.
rajput-k.
Machine Learning to Improve Accelerator Operation at SNS
Invited Talk Application of Machine Learning to improve accelerator operation, application of conditional models to predict anomalies at SNS accelerator.
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Model up-keep with Continual Learning for Particle Accelerators
Invited Tutorial Lecture on Model up-keep / Continual Learning for Particle Accelerators at MaLAPA 2024, Gyeongju, South Korea.
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Artificial Intelligence for the Electron Ion Collider (AI4EIC)
This is an opportune time for artificial intelligence (AI) to be included from the start at the upcoming Electron Ion Collider facility and in all phases that lead up to the …
allaire-c.
Anomaly Detection and Online Data Quality Monitoring
Online data quality monitoring with ML (Hydra) and Anomaly Detection on streaming data from physics and particle accelerator experiments.
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