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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.
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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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GPU Acceleration of Four-Site Water Models in LAMMPS

P. 565–573.
Nikolskiy V., Stegailov V.

In this work, a new algorithm was developed for calculating the fourpoint water model TIP4P on graphics accelerators. It was designed as a part of the flexible molecular dynamics modeling package LAMMPS in the library module “GPU”. In this paper we describe two approaches to implement the TIP4P model for GPU: 1) to divide the related computations between CPU and GPU; 2) to compute the interaction fully on the GPU. We verify the program, benchmark and profile it. The achieved speedup of interaction computation is about x7, acceleration of the entire calculation of about 55%.

Language: English
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Keywords: atomistic modelingLAMMPStip4pempirical potentialaccelerator
Publication based on the results of:
Methods for the analysis of the supercomputer efficiency, novel parallel algorithms for molecular dynamics calculations and modeling of transport processes in liquids and biomembranes (2020)

In book

Parallel Computing: Technology Trends
IOS Press, 2020.
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