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July 24, 2026
'Physics Is What the World Is Literally Built On'
Physicist Nina Dzhanayeva, recipient of a Vladimir Potanin Foundation scholarship, focuses her research on nanophotonics. In this interview for the HSE Young Scientists project, she discusses nanowells, scientific intuition, and how physics can help in making frangipane cream puffs.
July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
‘Science Is Universal-It Knows No Borders
Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.

 

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Проблемы реализации стохастических численных методов Рунге–Кутты

С. 318–320.
Gostev I. M., Геворкян М., Демидова А., Кулябов Д. С.

This article discusses stochastic numerical methods of Runge-Kutta type with weak and strong convergences for systems of stochastic differential equations in Itô form. At the beginning we give a brief overview of the stochastic numerical methods and information from the theory of stochastic differential equations. Then we motivate the approach to the implementation of these methods using source code generation. We discuss the implementation details and the used programming languages and libraries

Language: Russian
Full text
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Keywords: stochastic differential equationsстохастические дифференциальные уравненияpythonstochastic numerical methodsautomatic code generationPython; Juliathe template engine.стохастические численные методыавтоматическая генерация кодаJuliaшаблонизатор

In book

Материалы Всероссийской конференции с международным участием ИНФОРМАЦИОННО- ТЕЛЕКОММУНИКАЦИОННЫЕ ТЕХНОЛОГИИ И МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ ВЫСОКОТЕХНОЛОГИЧНЫХ СИСТЕМ
Материалы Всероссийской конференции с международным участием ИНФОРМАЦИОННО- ТЕЛЕКОММУНИКАЦИОННЫЕ ТЕХНОЛОГИИ И МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ ВЫСОКОТЕХНОЛОГИЧНЫХ СИСТЕМ
М.: Издательство РУДН, 2018.
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