Ghent University Academic Bibliography

Your filters: cql: author="Murari, Andrea" or (type any "bookEditor issueEditor" and editor="Murari, Andrea")

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- Journal Article
- A1
- open access
A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

Andrea Murari, Riccardo Rossi, Teddy Craciunescu, Jesús Vega, J. Mailloux, N. Abid, K. Abraham, P. Abreu, O. Adabonyan, P. Adrich, et al.

(2024) NATURE COMMUNICATIONS. 15(1).
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- Journal Article
- A1
- open access
Performance optimization of the electrostatic accelerator for DTTneutral beam injector

Fabio Veronese, Piero Agostinetti, Andrea Murari and Adriano Pepato

(2022) IEEE TRANSACTIONS ON PLASMA SCIENCE. 50(11). p.4033-4038
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- Journal Article
- A1
- open access
ELM regime classification by conformal prediction on an information manifold

Aqsa Shabbir (UGent) , Geert Verdoolaege (UGent) , Jesus Vega and Andrea Murari

(2015) IEEE TRANSACTIONS ON PLASMA SCIENCE. 43(12). p.4190-4199
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Clustering based on the geodesic distance on Gaussian manifolds for the automatic classication of disruptions

Andrea Murari, P Boutot, Jesús Vega, M Gelfusa, R Moreno, Geert Verdoolaege (UGent) and Peter de Vries

(2013) NUCLEAR FUSION. 53(3).
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Identification of confinement regimes in tokamak plasmas by conformal prediction on a probabilistic manifold

Geert Verdoolaege (UGent) , Jesús Vega, Andrea Murari and Guido Van Oost (UGent)

(2012) IFIP Advances in Information and Communication Technology. 382. p.244-253
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Modeling fusion data in probabilistic metric spaces: applications to the identification of confinement regimes and plasma disruptions

Geert Verdoolaege (UGent) , Giorgos Karagounis, Andrea Murari, Jesus Vega and Guido Van Oost (UGent)

(2012) FUSION SCIENCE AND TECHNOLOGY. 62(2). p.356-365
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- Conference Paper
- C3
Prediction of disruptions at JET using the geodesic distance between wavelet distributions

Geert Verdoolaege (UGent) , Giorgos Karagounis, Andrea Murari, Jesús Vega and Guido Van Oost (UGent)

(2012) 27th Symposium of Fusion Technology, Abstracts.
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Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces

Andrea Murari, Jesús Vega, P Boutot, B Cannas, S Dormido-Canto, A Fanni, J López, R Moreno, A Pau, G Sias, et al.

(2012) 24th IAEA Fusion Energy Conference, Proceedings.

Academic Bibliography

A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

Performance optimization of the electrostatic accelerator for DTTneutral beam injector

ELM regime classification by conformal prediction on an information manifold

Clustering based on the geodesic distance on Gaussian manifolds for the automatic classication of disruptions

Identification of confinement regimes in tokamak plasmas by conformal prediction on a probabilistic manifold

Modeling fusion data in probabilistic metric spaces: applications to the identification of confinement regimes and plasma disruptions

Prediction of disruptions at JET using the geodesic distance between wavelet distributions

Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces

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A control oriented strategy of disruption prediction to avoid the configuration collapse of tokamak reactors

Performance optimization of the electrostatic accelerator for DTTneutral beam injector

ELM regime classification by conformal prediction on an information manifold

Clustering based on the geodesic distance on Gaussian manifolds for the automatic classi cation of disruptions

Identification of confinement regimes in tokamak plasmas by conformal prediction on a probabilistic manifold

Modeling fusion data in probabilistic metric spaces: applications to the identification of confinement regimes and plasma disruptions

Prediction of disruptions at JET using the geodesic distance between wavelet distributions

Latest developments in data analysis tools for disruption prediction and for the exploration of multimachine operational spaces

Clustering based on the geodesic distance on Gaussian manifolds for the automatic classication of disruptions