Machine learning and Bayesian inference in nuclear fusion research: an overview

Abstract

This article reviews applications of Bayesian inference and machine learning 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, machine learning, 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 machine learning 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 machine learning play along, mitigating each other’s drawbacks: the former is made more efficient, the latter more interpretable.

Cold Atmospheric Plasma Medicine: Applications, Challenges, and Opportunities for Predictive Control

“…We propose that advances in data science, particularly artificial intelligence and machine learning (AI/ML) techniques, coupled with data curation and protocols for sharing amongst the plasma-medicine community, can accelerate the progress of the field towards greater societal impact. AI/ML has demonstrated considerable promise in materials science [129][130][131] and nuclear fusion [132][133][134], among many other fields. The commonalities among these applications are as follows: (1) a vast information database of many independent investigators (e.g., condensed matter science) or (2) a large database from a singular experiment with variable parameters (e.g., DIII-D).…”

A Gaussian process guide for signal regression in magnetic fusion

“…At JET, GPs have been used to infer electron cyclotron emission spectra [27], and high resolution Thomson scattering and far infrared interferometer data in [28], while work in [29] is focused on using GPRs to quantify edge plasma evolution from experimental data. A general overview of Bayesian inference in fusion can be found in [10,30].…”

“…In most applications of GPR and in this work, a GP is used to approximate an underlying deterministic, but uncertain, function, which is inferred from a given set of observations. The resulting calibrated GPs can be readily utilized to inform theoretical analyses [9,10], by inputting experimental data fits to theoretical and numerical physics models, and can also be powerful in guiding experimental exploration, optimization, and design.…”

Gaussian process-based quasi-coherent noise suppression in magnetic confinement devices with superconductors

“…Note that the GP achieves non-parametric regression using a multivariate normal distribution and a covariance kernel function taking into account of correlations determined by the data distance. The GP is a widely adopted non-parametric model in the fusion community when a parametric model describing measured signals is not suitable or limited such as plasma profile fittings [22][23][24][25][26] or tomographic reconstructions [27][28][29][30].…”