Machine Learning Applications Enabling Fusion Energy: Recent Developments

Abstract

Over the last few years, machine learning helped to develop advanced capabilities for fusion energy over a broad range of domains. This includes advanced algorithms to extract information from fusion diagnostics, enhanced algorithms for plasma state estimation and control, accelerated simulation tools to improve predictive capabilities, and expanded modeling capabilities for fusion materials design. This topical collection covers recent developments in machine learning applied research further enabling the path to fusion energy; in particular it covers a wide breadth of fusion subfields – from inertial confinement fusion, to magnetically confined plasma, including high temperature superconducting magnet design and optimization. This editorial summarizes the collection while also providing a critical outlook on how machine learning can be used in the future to accelerate the development of fusion energy as a reliable energy source.