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News
October 30, 2026
‘This Format Turned Each Presentation Into a Collaborative Effort to Assess the Robustness of the Study
On October 16, 2026, HSE ICEF hosted the Eighth International Conference on Applied Economics. Held fully on-site, the conference brought researchers from HSE University, the New Economic School, Wuhan University, and Shandong University together in Moscow. The programme was organised around three overarching themes— ‘Environment, Resources and Policy,’ ‘Migration, Labour, and Communication,’ and ‘Consumption, Behaviour, and Climate—offering an opportunity to see how similar economic mechanisms operate in China, Pakistan, the United States, and Russia.
October 23, 2026
‘I Always Conduct Research on Topics That Genuinely Matter to Me
Anastasiia Deeva studies the socio-economic consequences of excess weight in Russia. In an interview for the Young Scientists of HSE University project, she spoke about why beauty cannot be measured, her desire to write a novel, ‘Dostoevskian’ burnout, and working at the hotel where The Shining was filmed.
October 19, 2026
'Cultural Studies Seeks to Understand What Lies Beneath the Surface of Everyday Things'
Maxim Zhiganov has been exploring different aspects of sound for many years—first in relation to his native Perm and then on a broader, global scale. In this interview for the HSE Young Scientists project, he talks about sound maps, the special issue of the journal Logos, and why it is important to make the familiar less clear and obvious.

 

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Scalable machine learning approach to disordered 𝑠-wave superconductors

Physical Review B: Condensed Matter and Materials Physics. 2026. Vol. 113. No. 2. Article 024515.
Vyacheslav D. Neverov, Lukyanov A., Andrey V. Krasavin, Vagov A.

We develop a neural network approach to solve the self-consistent Bogoliubov-de Gennes equations in strongly disordered s-wave superconductors. The method accurately reproduces inhomogeneous gap distributions and generalizes to system sizes far larger than those used in training. It reduces computational scaling from O(N6 ) to O(N2), enabling quantitative analysis of percolation phenomena and the superconductor-insulator transition with much smaller computational efforts.

Research target: Physics Computer Science Materials Technologies
Language: English
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Keywords: машинное обучениенейронные сетиneural networkspercolationmachine learningперколяцияdisordered superconductorssuperconductor–insulator transitionнеупорядоченные сверхпроводникипереход сверхпроводник–изолятор
Publication based on the results of:
Advanced materials and technologies (2025)
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