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Project
Protecting future fusion power plants – Development of efficient and real-time plasma disruption predictors for ITER by combining advanced data-driven models with physics insights
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Project Team
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Outputs & Impact
Publications & research data ( 6 )
Cluster-based input weight initialization for echo state networks
Peter Steiner
Azarakhsh Jalalvand
Peter Birkholz
A1
Journal Article
in
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2023
Hybrid static-sensory data modeling for prediction tasks in basic oxygen furnace process
Davi Alberto Sala
Andy Van Yperen-De Deyne
Erik Mannens
Azarakhsh Jalalvand
A1
Journal Article
in
APPLIED INTELLIGENCE
2023
Exploring data-driven models for spatiotemporally local classification of Alfven eigenmodes
Alan A. Kaptanoglu
Azarakhsh Jalalvand
Alvin Garcia
Max E. Austin
Geert Verdoolaege
Jeff Schneider
Christopher J. Hansen
Steven L. Brunton
William W. Heidbrink
Egemen Kolemen
A1
Journal Article
in
NUCLEAR FUSION
2022
PyRCN : a toolbox for exploration and application of Reservoir Computing Networks
Peter Steiner
Azarakhsh Jalalvand
Simon Stone
Peter Birkholz
A1
Journal Article
in
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
2022
Real-time and adaptive reservoir computing with application to profile prediction in fusion plasma
Azarakhsh Jalalvand
Joseph Abbate
Rory Conlin
Geert Verdoolaege
Egemen Kolemen
A1
Journal Article
in
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
2022
Alfven eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks
Azarakhsh Jalalvand
Alan A. Kaptanoglu
Alvin, V Garcia
Andrew O. Nelson
Joseph Abbate
Max E. Austin
Geert Verdoolaege
Steven L. Brunton
William W. Heidbrink
Egemen Kolemen
A1
Journal Article
in
NUCLEAR FUSION
2022
Activities ( 0 )
Results
Impact narratives ( 0 )
Patents ( 0 )