Roadmap on artificial intelligence and big data techniques for superconductivity. (24th February 2023)

• Record Type: Journal Article
• Authors: Yazdani-Asrami, Mohammad; Song, Wenjuan; Morandi, Antonio; De Carne, Giovanni; Murta-Pina, Joao; Pronto, Anabela; Oliveira, Roberto; Grilli, Francesco; Pardo, Enric; Parizh, Michael; Shen, Boyang; Coombs, Tim; Salmi, Tiina; Wu, Di; Coatanea, Eric; Moseley, Dominic A; Badcock, Rodney A; Zhang, Mengjie; Marinozzi, Vittorio; Tran, Nhan; Wielgosz, Maciej; Skoczeń, Andrzej; Tzelepis, Dimitrios; Meliopoulos, Sakis; Vilhena, Nuno; Sotelo, Guilherme; Jiang, Zhenan; Große, Veit; Bagni, Tommaso; Mauro, Diego; Senatore, Carmine; Mankevich, Alexey; Amelichev, Vadim; Samoilenkov, Sergey; Yoon, Tiem Leong; Wang, Yao; Camata, Renato P; Chen, Cheng-Chien; Madureira, Ana Maria; Abraham, Ajith;
• Is Part Of: Superconductor science & technology. Volume 36:Number 4(2023)
• Publication Date: 2023-02-24
• Subjects: applied superconductivity -- artificial intelligence -- big data -- deep learning -- machine learning -- neural network; Superconductivity -- Periodicals; Superconductors -- Periodicals; 537.623
• DOI: 10.1088/1361-6668/acbb34
• Languages: English
• ISSNs: 0953-2048

Abstract

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.