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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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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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Identification of montmorillonite particle edge orientations by atomic-force microscopy

Applied Clay Science. 2020. Vol. 186. Article 105442.
Kraevsky S. V., Tournassat C., Vayer M., Warmont F., Grangeon S., Ngouana-Wakou B. F., Kalinichev A. G.

Statistical information on the edge surface area and edge crystallographic orientation of clay nanoparticle surfaces is essential for proper accounting of the protonation-deprotonation reactions as a part of mechanistic surface complexation models. A combination of atomic-force microscopy (AFM) measurements and molecular dynamics computer simulations made it possible to quantify the relative contributions of the most frequently occurring montmorillonite edge surfaces to the total edge surface area. Edge surfaces normal to the [110] and [010] crystallographic directions are found to be the most abundant (~60% and ~20%, respectively), in agreement with previous estimations.

Research target: Earth Sciences Chemical Technologies Chemistry
Priority areas: engineering science
Language: English
DOI
Text on another site
Keywords: молекулярная динамикаatomic force microscopyатомно-силовая микроскопиянаноматериалыnanomaterialsАтомистическое и многомасштабное моделированиеnanoparticlesнаночастицыatomistic simulations molecular dynamics
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)
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