In a world increasingly facing new challenges at the forefront of plasma scientific research and technological innovation, CNR and ISTP pledge progress and achieve an impact in the integration of research into societal practices and policy
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machine learning predictors
Showing all 3 results
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Nuclear fusion 61 (3), pp. 036027-1 - 036027-20 Year: 2021 DOI: 10.1088/1741-4326/abc9f3
Stacking of predictors for the automatic classification of disruption types to optimize the control logic
Murari, A.; Rossi, R.; Lungaroni, M.; Baruzzo, M.; Gelfusa, M. -
Nuclear fusion 60 (5), pp. 056003-1 - 056003-18 Year: 2020 DOI: 10.1088/1741-4326/ab77a6
On the transfer of adaptive predictors between different devices for both mitigation and prevention of disruptions
Murari A.; Rossi R.; Peluso E.; Lungaroni M.; Gaudio P.; Gelfusa M.; Ratta G.; Vega J. -
Nuclear fusion (Online) 59 (8), pp. 086037-1 - 086037-11 Year: 2019 DOI: 10.1088/1741-4326/ab1ecc
Adaptive learning for disruption prediction in non-stationary conditions
Murari A.; Lungaroni M.; Gelfusa M.; Peluso E.; Vega J.
