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Machine learning and Bayesian inference in nuclear fusion research: an overview
Ist Teil von
Plasma physics and controlled fusion, 2023-05, Vol.65 (5), p.53001
Ort / Verlag
IOP Publishing
Erscheinungsjahr
2023
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
Abstract
This article reviews applications of Bayesian inference and machine learning (ML) in nuclear fusion research. Current and next-generation nuclear fusion experiments require analysis and modelling efforts that integrate different models consistently and exploit information found across heterogeneous data sources in an efficient manner. Model-based Bayesian inference provides a framework well suited for the interpretation of observed data given physics and probabilistic assumptions, also for very complex systems, thanks to its rigorous and straightforward treatment of uncertainties and modelling hypothesis. On the other hand, ML, in particular neural networks and deep learning models, are based on black-box statistical models and allow the handling of large volumes of data and computation very efficiently. For this reason, approaches which make use of ML and Bayesian inference separately and also in conjunction are of particular interest for today’s experiments and are the main topic of this review. This article also presents an approach where physics-based Bayesian inference and black-box ML play along, mitigating each other’s drawbacks: the former is made more efficient, the latter more interpretable.