Roadmap on artificial intelligence and big data techniques for superconductivity

Yazdani-Asrami, Mohammad and Song, Wenjuan and Morandi, Antonio and De Carne, Giovanni and Murta-Pina, Joao and Pronto, Anabela and Oliveira, Roberto and Grilli, Francesco and Pardo, Enric and Parizh, Michael and Shen, Boyang and Coombs, Tim and Salmi, Tiina and Wu, Di and Coatanea, Eric and Moseley, Dominic A and Badcock, Rodney A and Zhang, Mengjie and Marinozzi, Vittorio and Tran, Nhan and Wielgosz, Maciej and Skoczeń, Andrzej and Tzelepis, Dimitrios and Meliopoulos, Sakis and Vilhena, Nuno and Sotelo, Guilherme and Jiang, Zhenan and Große, Veit and Bagni, Tommaso and Mauro, Diego and Senatore, Carmine and Mankevich, Alexey and Amelichev, Vadim and Samoilenkov, Sergey and Yoon, Tiem Leong and Wang, Yao and Camata, Renato P and Chen, Cheng-Chien and Madureira, Ana Maria and Abraham, Ajith (2023) Roadmap on artificial intelligence and big data techniques for superconductivity. Superconductor Science and Technology, 36 (4). 043501. ISSN 0953-2048 (https://doi.org/10.1088/1361-6668/acbb34)

[thumbnail of Yazdani-etal-SST-2023-Roadmap-on-artificial-intelligence-and-big-data-techniques]
Preview
 Text. Filename: Yazdani_etal_SST_2023_Roadmap_on_artificial_intelligence_and_big_data_techniques.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (6MB)| Preview

Abstract

This paper presents a roadmap to the application of AI techniques and big data (BD) for different modelling, design, monitoring, manufacturing and operation purposes of different superconducting applications. To help superconductivity researchers, engineers, and manufacturers understand the viability of using AI and BD techniques as future solutions for challenges in superconductivity, a series of short articles are presented to outline some of the potential applications and solutions. These potential futuristic routes and their materials/technologies are considered for a 10–20 yr time-frame.

CORE (COnnecting REpositories)