4 papers
Evolutionary Two-Stage Hyperparameter Optimization Strategies for Physics-Informed Neural Networks
Fedor Buzaev, Dmitry Efremenko, Egor Bugaev +4
Physics-Informed Neural Networks (PINNs) solve Partial Differential Equations (PDEs) by embedding physical laws into neural network training. However, their performance suffers fro…
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…
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…