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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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OpenDust: A fast GPU-accelerated code for the calculation of forces acting on microparticles in a plasma flow

Computer Physics Communications. 2023. Vol. 288. Article 108746.
Kolotinskii D., Timofeev A.

We present the first open-source, GPU-based code for complex plasmas. The code, OpenDust, pursues to provide researchers, both experimenters and theorists, a user-friendly and high-performance tool for self-consistent calculation forces, acting on microparticles, and microparticles' charges in a plasma flow. OpenDust performance originates from a highly-optimized Cuda back-end and performs self-consistent calculations of plasma flow around microparticles in seconds. This code outperforms all available codes for self-consistent complex plasma simulation. Moreover, OpenDust can also be used for the simulation of larger systems of dust microparticles, which was previously unavailable. OpenDust interface is written in Python, which provides ease-of-use and simple installation from the Conda repository.

Research target: Computer Science Physics
Language: English
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Keywords: пылевая плазмаграфический ускорительGPU-accelerated softwaredusty plasma
Publication based on the results of:
New technologies of supercomputer modeling in material science (2023)
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