Cristina Rea - Data-driven learning for disruption prevention and performance optimization

Recorded 15 April 2026. Cristina Rea of the Massachusetts Institute of Technology presents "Data-driven learning for disruption prevention and performance optimization" at IPAM's Learning Models from Data for Multi-Fidelity Fusion Plasma Physics Workshop.
Abstract: This seminar will focus on the development of interpretable Machine Learning (ML) driven solutions for two critical challenges in magnetic confinement fusion: (1) real-time monitoring of proximity to plasma stability boundaries and (2) the optimization of plasma trajectories. Modeling and experimental results carried out at DIII-D and TCV will be presented. At the core of these advancements is a shift towards data-efficient Scientific Machine Learning: this hybrid modeling approach enables interpretability by coupling the known physics models to ML corrections to advance the reliability, efficiency, and scalability of ML-enabled solutions for both existing and next-generation fusion devices.
Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-ii-learning-models-from-data-for-multi-fidelity-fusion-plasma-physics/ Receive SMS online on sms24.me

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