CPS-FR: Machine learning and Bayesian modeling at Wendelstein 7-X

Andrea Pavone

Institute of Plasma Physics

Tuesday, August 23, 2022



PSFC Seminars

Abstract: Bayesian inference provides an elegant framework to learn from data. Machine learning aims at developing algorithms which can learn effectively from data. I will show how the learning framework provided by Bayesian methods can support and be supported by machine learning solutions, especially modern deep learning algorithms. Through applications related to the Wendelstein 7-X fusion experiment, I will demonstrate how Bayesian inference can enhance the exploitation of interdependent heterogeneous sources of information, such as plasma diagnostic measurements, in a complex system through physics-based modeling and conventional inference methods (MCMC, MAP), and how it can benefits from recent advances based on deep learning to scale up to the large amount of data and systems found in nowadays fusion experiments. I will also introduce uncertainty quantification methods which can help making 'black-box' approaches, such as deep learning, a tool which can be relied upon in real-world applications. Here, the Bayesian framework will prove useful once again.

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