699 citations · 2.7k across the 97 of their papers we have counts for
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Reinforcement Learning for Accelerator Beamline Control: a simulation-based approach
Anwar Ibrahim, Alexey Petrenko, Maxim Kaledin +3
Particle accelerators play a pivotal role in advancing scientific research, yet optimizing beamline configurations to maximize particle transmission remains a labor-intensive task…
Dynamic Functional Connectivity Features for Brain State Classification: Insights from the Human Connectome Project
Valeriya Kirova, Dzerassa Kadieva, Daniil Vlasenko +2
We analyze functional magnetic resonance imaging (fMRI) data from the Human Connectome Project (HCP) to match brain activities during a range of cognitive tasks. Our findings demon…
Approach to Finding a Robust Deep Learning Model
Alexey Boldyrev, Fedor Ratnikov, Andrey Shevelev
The rapid development of machine learning (ML) and artificial intelligence (AI) applications requires the training of large numbers of models. This growing demand highlights the im…
Production of protons, deuterons and tritons in argon-nucleus interactions at 3.2 AGeV
S. Afanasiev, G. Agakishiev, A. Aleksandrov +200
Results of the BM@N experiment at the Nuclotron/NICA complex on the production of protons, deuterons and tritons in interactions of an argon beam of 3.2 AGeV with fixed targets of…
Optimisation of the Accelerator Control by Reinforcement Learning: A Simulation-Based Approach
Anwar Ibrahim, Denis Derkach, Alexey Petrenko +2
Optimizing accelerator control is a critical challenge in experimental particle physics, requiring significant manual effort and resource expenditure. Traditional tuning methods ar…