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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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Two-temperature molecular dynamics simulations of crystal growth in a tungsten supercooled melt

Journal of Physics: Condensed Matter. 2022. Vol. 35. No. 1. Article 015401.
Joás Grossi, Pisarev V.

In this work we use the two-temperature model coupled to molecular dynamics with sinks at the boundaries of the electronic subsystem to study crystal-growth rate in a quasi one-dimensional W crystal into a supercooled melt. The possibility of varying the extension of the electronic grid along with the sinks allows a more realistic description of the electronic thermal transport away from the system, providing a considerable heat dissipation from the crystallization front. Based on this approach, our results regarding crystal-growth rates are not affected even if the size of the system is changed. Moreover, comparisons are established with respect to molecular dynamics and standard two-temperature model simulations. For these comparisons between models, something remarkable is found, and it is that the temperature and the value of the maximum growth rate are the same. In contrast, the inclusion of sinks has a great impact with respect to the standard approaches specially reflected at low temperatures, where a frustration of the liquid-crystal interface dynamics is seen until a state of zero crystal growth is reached, which is not possible to characterize quantitatively since a kind of stochastic behavior is present.

Research target: Physics
Language: English
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Keywords: tungstenmetallic glasses crystal growthTTM-MDliquid-crystal interface
Publication based on the results of:
New technologies of supercomputer modeling in material science (2023)
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