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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Simulation of a Multifractal Turbulent Electromagnetic Field in Cosmic Plasma

Cosmic Research, USA. 2023. Vol. 61. No. 2. P. 113–119.
Levashov N. N., Popov V., Malova H. V., Zelenyi L. M.

A two-dimensional model of a multifractal turbulent electromagnetic field is proposed that allows flexibly varying the width of the multifractal spectrum and the level of intermittency. The electromagnetic field is modeled using a superposition of wavelets that are distributed uniformly throughout the computational domain. By means of a special distribution of amplitudes, we ensure that the resulting field is multifractal and intermittent. This model was used to study the effect of multifractality and intermittency on the acceleration of charged particles in a turbulent field in the Earth’s magnetotail. It was shown that, in the case of a multifractal field, individual particles are able to achieve higher energy values in comparison with monofractal turbulence.

Research target: Computer Science Mathematics Physics
Language: English
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Keywords: космическая плазмаCosmic PlasmaMultifractal Turbulent Electromagnetic FieldТурбулентное электромагнитное поле
Publication based on the results of:
Critical phenomena in complex systems: analysis, modeling, detection, forecasting (2023)
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