Moscow University Physics Bulletin. Vol. 79, Suppl. 2, 2024
Application of Kolmogorov–Arnold Networks in High Energy Physics
Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning
Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks
Machine Learning Approach in the Prediction of Differential Cross Sections and Structure Functions of Single Pion Electroproduction in the Resonance Region
Gamma/Hadron Separation in the TAIGA Experiment with Neural Network Methods
Encoding of Input Signals in Terms of Path Complexes in Spiking Neural Networks
Application of Neural Networks for Path Integrals Computation in Relativistic Quantum Mechanics
Enhanced Image Clustering with Random-Bond Ising Models Using LDPC Graph Representations and Nishimori Temperature
Neural Network Modeling of Optical Solitons Described by Generalized Nonlinear Schrödinger Equations
Application of Convolutional Neural Networks for Extensive Air Shower Separation in the SPHERE-3 Experiment
Generation of Grid Surface Detector Data in the Telescope Array Experiment Using Neural Networks
Machine Learning in Gamma Astronomy
Multidimensional Global Optimization of Detector Systems Using the Example of Muon Shield in the SHiP Experiment
Solving Problems of Mathematical Physics on Radial Basis Function Networks
Reconstruction of Energy and Arrival Directions of UHECRs Registered by Fluorescence Telescopes with Neural Networks
Evaluating EAS Directions from TAIGA HiSCORE Data Using Fully Connected Neural Networks
Identification of Air Pollutants with Thermally Modulated Metal Oxide Semiconductor Gas Sensors through Machine Learning Based Response Models
An Overview of Machine Learning and Deep Learning Applications in Earth Sciences in 2024: Achievements and Perspectives
Pointwise and Complex Quality Metrics in Atmospheric Modeling: Methods and Approaches
Probabilistic Programming Methods for Reconstruction of Multichannel Imaging Detector Events: ELVES and TRACKS
Machine Learning Methods for Statistical Prediction of PM2.5 in Urban Agglomerations with Complex Terrain, Using Grenoble As an Example
Approximation of Spatial and Temporal Variability of the Urban Heat Island in Moscow Using Machine Learning
Forecasting the State of the Earth’s Magnetosphere Using a Special Algorithm for Working with Multidimensional Time Series
Diagnostics of Geoinduced Currents in High Latitude Power Systems Using Machine Learning Methods
Engineering Point Defects in MoS2 for Tailored Material Properties Using Large Language Models
A Transformer Architecture for Risk Analysis of Group Effects of Food Nutrients
Development of a Multimodal Photoluminescent Carbon Nanosensor for Metal Ions in Water Using Artificial Neural Networks
Comparative Analysis of the Procedures to Forecast the Kp Geomagnetic Index by Machine Learning
Prediction of Defect Structure in MoS2 by Given Properties
Feature Selection Methods for Deep Learning Models of Soft Sensors in Oil Refining
An Original Algorithm for Classifying Premotor Potentials in Electroencephalogram Signal for Neurorehabilitation Using a Closed-Loop Brain–Computer Interface
Nonlinear Relevance Estimation of Multicollinear Features for Reducing the Input Dimensionality of Optical Spectroscopy Inverse Problem
Machine Learning for FARICH Reconstruction at NICA SPD
Improving Physics-Informed Neural Networks via Quasiclassical Loss Functionals
Neural Operators for Hydrodynamic Modeling of Underground Gas Storages
Gaussian Process Based Prediction of Density Distribution in Core of Research Nuclear Reactor
Spiking Neural Network Actor–Critic Reinforcement Learning with Temporal Coding and Reward-Modulated Plasticity