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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September 9, 2025 9:30 AM — 10:00 AM
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1 min read
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.
Probabilistic Envelope based Visualization for Monitoring Drilling Well Data Logging
This paper presents a probabilistic envelope-based visualization technique for monitoring drilling well data logging. The method enables effective identification of anomalies and …
rajput-k.
Uncertainty Quantification for Rare Events in Scientific Applications
Uncertainty Quantification for Rare Events (Out-of-Distribution Detection) via Gaussian Process Approximation in Scientific Applications
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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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