Predicting disruptive instabilities in controlled fusion plasmas through deep learning

Machine learning phases of matter

(2017) Juan Carrasquilla et al.   Nature Physics

Overview of the JET results in support to ITER

(2017) X. Litaudon et al.   NUCLEAR FUSION

Solving the quantum many-body problem with artificial neural networks

(2017) Giuseppe Carleo et al.   SCIENCE

Disruption Prediction on JET during the ILW Experimental Campaigns

(2016) R. Moreno et al.   FUSION SCIENCE AND TECHNOLOGY

Requirements for Triggering the ITER Disruption Mitigation System

(2016) P. C. de Vries et al.   FUSION SCIENCE AND TECHNOLOGY

The Power of an Idea: The International Impacts of the Grand Challenges for Engineering

(2016) C.D. Mote et al.   Engineering

Disruptions in ITER and strategies for their control and mitigation

(2015) M. Lehnen et al.   JOURNAL OF NUCLEAR MATERIALS

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Deep learning

(2015) Yann LeCun et al.   NATURE

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

(2013) Jesús Vega et al.   FUSION ENGINEERING AND DESIGN

Improved feature selection based on genetic algorithms for real time disruption prediction on JET

(2012) G.A. Rattá et al.   FUSION ENGINEERING AND DESIGN

Survey of disruption causes at JET

(2011) P.C. de Vries et al.   NUCLEAR FUSION

JET ITER-like wall—overview and experimental programme

(2011) G F Matthews et al.   PHYSICA SCRIPTA

Commissioning of electron cyclotron emission imaging instrument on the DIII-D tokamak and first data

(2010) B. Tobias et al.   REVIEW OF SCIENTIFIC INSTRUMENTS

Statistical analysis of disruptions in JET

(2009) P.C. de Vries et al.   NUCLEAR FUSION

Prototype of an adaptive disruption predictor for JET based on fuzzy logic and regression trees

(2008) A. Murari et al.   NUCLEAR FUSION

Feature extraction for improved disruption prediction analysis at JET

(2008) G. A. Rattá et al.   REVIEW OF SCIENTIFIC INSTRUMENTS