Advances in nuclear fusion and the role of artificial intelligence through STRANAI

Authors

DOI:

https://doi.org/10.5377/pc.v1i21.23099

Keywords:

nuclear fusion, STRANAI, Artificial intelligence, Fusion reactors

Abstract

Nuclear fusion represents a promising energy alternative, capable of producing clean, abundant, and reliable energy with lower environmental impacts than fossil fuels. However, the practical implementation of this technology faces significant challenges, including maintaining elevated temperatures and pressures, plasma stability, energy efficiency, and material durability. The STRANAI Strategic Network integrates artificial intelligence across multiple research lines to address these challenges, including automatic detection of physical events through images and videos, automation of turbulence analysis, development of explainable and physics-based artificial intelligence models, creation of multi-machine repositories, and the design of predictive disruption models and plasma control strategies. This interdisciplinary approach optimizes the safety, efficiency, and reliability of future fusion reactors and accelerates the transition toward commercial fusion power plants. This article reviews recent advances in nuclear fusion, existing reactor types, and the technological challenges addressed through artificial intelligence applications.

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Author Biographies

K.A. Raudales, Universidad Nacional de San Juan; San Juan Capital, Argentina.

Instituto de Energia Electrica (IEE); Universidad Nacional de San Juan; San Juan Capital J5400; San Juan; Argentina.

D.A. Sosa-Urquía, Universidad Nacional Autónoma de Honduras - Ciudad Universitaria. Tegucigalpa, Honduras

Departamento de Materia Condensada; Universidad Nacional Autonoma de Honduras-Ciudad Universitaria; Tegucigalpa 11101; Honduras

Published

2026-06-24

How to Cite


Raudales, K., & Sosa-Urquía, D. (2026). Advances in nuclear fusion and the role of artificial intelligence through STRANAI. Portal De La Ciencia, (21), 105–119. https://doi.org/10.5377/pc.v1i21.23099

Issue

Section

Original scientific article