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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.
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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.
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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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Disjoining Pressure Decay Length in Room-Temperature Ionic Liquids: A Molecular Simulation Study

Journal of Physical Chemistry B. 2024. Vol. 128. No. 9. P. 2215–2218.
Darya L. Gurina, Odintsova E., Yury A. Budkov

We present all-atom molecular simulations to investigate the behavior of 1-ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) in negatively charged carbon nanopores of different widths (h = 5÷15 nm) and lengths (l = 4÷10 nm). The goal of our study was to determine how the disjoining pressure varies as a function of the pore width at different lengths and to understand the influence of edge effects. Our results show that the edge effect decreases as the pore length increases. Using an exponential function, we can approximate the disjoining pressure at large pore widths and use this approximation to estimate the decay length that can correlate with the electrostatic screening length. The results agreed well with those of previous experimental studies.

Research target: Chemistry Physics
Language: English
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Keywords: Disjoining pressureРасклинивающее давлениеИонные жидкостиклассическая молекулярная динамикаRoom temperature ionic liquidsclassical molecular dynamics
Publication based on the results of:
Computer modeling and machine learning of actual problems of physics (2024)
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