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-====== Special Issue: Deep Learning in Computational Physics ======
-**Moscow University Physics Bulletin. Vol. 79, Suppl. 2, 2024**
-{{ :dlcp2024:bphmcont.pdf |Contents}}
-{{ :dlcp2024:bphm585.pdf |Application of Kolmogorov–Arnold Networks in High Energy Physics}} \\
-{{ :dlcp2024:bphm591.pdf |Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning}} \\
-{{ :dlcp2024:bphm598.pdf |Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks}} \\
-{{ :dlcp2024:bphm608.pdf |Machine Learning Approach in the Prediction of Differential Cross Sections and Structure Functions of Single Pion Electroproduction in the Resonance Region}} \\
-{{ :dlcp2024:bphm622.pdf |Gamma/Hadron Separation in the TAIGA Experiment with Neural Network Methods}} \\
-{{ :dlcp2024:bphm630.pdf |Encoding of Input Signals in Terms of Path Complexes in Spiking Neural Networks}} \\
-{{ :dlcp2024:bphm639.pdf |Application of Neural Networks for Path Integrals Computation in Relativistic Quantum Mechanics}} \\
-{{ :dlcp2024:bphm647.pdf |Enhanced Image Clustering with Random-Bond Ising Models Using LDPC Graph Representations and Nishimori Temperature}} \\
-{{ :dlcp2024:bphm666.pdf |Neural Network Modeling of Optical Solitons Described by Generalized Nonlinear Schrödinger Equations}} \\
-{{ :dlcp2024:bphm676.pdf |Application of Convolutional Neural Networks for Extensive Air Shower Separation in the SPHERE-3 Experiment}} \\
-{{ :dlcp2024:bphm684.pdf |Generation of Grid Surface Detector Data in the Telescope Array Experiment Using Neural Networks}} \\
-{{ :dlcp2024:bphm690.pdf |Machine Learning in Gamma Astronomy}} \\
-{{ :dlcp2024:bphm700.pdf |Multidimensional Global Optimization of Detector Systems Using the Example of Muon Shield in the SHiP Experiment}} \\
-{{ :dlcp2024:bphm706.pdf |Solving Problems of Mathematical Physics on Radial Basis Function Networks}} \\ \\
-{{ :dlcp2024:bphm712.pdf |Reconstruction of Energy and Arrival Directions of UHECRs Registered by Fluorescence Telescopes with Neural Networks}} \\
-{{ :dlcp2024:bphm724.pdf |Evaluating EAS Directions from TAIGA HiSCORE Data Using Fully Connected Neural Networks}} \\
-{{ :dlcp2024:bphm731.pdf |Identification of Air Pollutants with Thermally Modulated Metal Oxide Semiconductor Gas Sensors through Machine Learning Based Response Models}} \\
-{{ :dlcp2024:bphm739.pdf |An Overview of Machine Learning and Deep Learning Applications in Earth Sciences in 2024: Achievements and Perspectives}} \\
-{{ :dlcp2024:bphm750.pdf |Pointwise and Complex Quality Metrics in Atmospheric Modeling: Methods and Approaches}} \\
-{{ :dlcp2024:bphm765.pdf |Probabilistic Programming Methods for Reconstruction of Multichannel Imaging Detector Events: ELVES and TRACKS}} \\
-{{ :dlcp2024:bphm774.pdf |Machine Learning Methods for Statistical Prediction of PM2.5 in Urban Agglomerations with Complex Terrain, Using Grenoble As an Example}} \\
-{{ :dlcp2024:bphm784.pdf |Approximation of Spatial and Temporal Variability of the Urban Heat Island in Moscow Using Machine Learning}} \\
-{{ :dlcp2024:bphm798.pdf |Forecasting the State of the Earth’s Magnetosphere Using a Special Algorithm for Working with Multidimensional Time Series}} \\
-{{ :dlcp2024:bphm807.pdf |Diagnostics of Geoinduced Currents in High Latitude Power Systems Using Machine Learning Methods}} \\
-{{ :dlcp2024:bphm818.pdf |Engineering Point Defects in MoS2 for Tailored Material Properties Using Large Language Models}} \\
-{{ :dlcp2024:bphm828.pdf |A Transformer Architecture for Risk Analysis of Group Effects of Food Nutrients}} \\
-{{ :dlcp2024:bphm844.pdf |Development of a Multimodal Photoluminescent Carbon Nanosensor for Metal Ions in Water Using Artificial Neural Networks}} \\
-{{ :dlcp2024:bphm854.pdf |Comparative Analysis of the Procedures to Forecast the Kp Geomagnetic Index by Machine Learning}} \\
-{{ :dlcp2024:bphm866.pdf |Prediction of Defect Structure in MoS2 by Given Properties}} \\
-{{ :dlcp2024:bphm872.pdf |Feature Selection Methods for Deep Learning Models of Soft Sensors in Oil Refining}} \\
-{{ :dlcp2024:bphm890.pdf |An Original Algorithm for Classifying Premotor Potentials in Electroencephalogram Signal for Neurorehabilitation Using a Closed-Loop Brain–Computer Interface}} \\
-{{ :dlcp2024:bphm898.pdf |Nonlinear Relevance Estimation of Multicollinear Features for Reducing the Input Dimensionality of Optical Spectroscopy Inverse Problem}} \\
-{{ :dlcp2024:bphm906.pdf |Machine Learning for FARICH Reconstruction at NICA SPD}} \\
-{{ :dlcp2024:bphm914.pdf |Improving Physics-Informed Neural Networks via Quasiclassical Loss Functionals}} \\
-{{ :dlcp2024:bphm922.pdf |Neural Operators for Hydrodynamic Modeling of Underground Gas Storages}} \\
-{{ :dlcp2024:bphm935.pdf |Gaussian Process Based Prediction of Density Distribution in Core of Research Nuclear Reactor}} \\
-{{ :dlcp2024:bphm944.pdf |Spiking Neural Network Actor–Critic Reinforcement Learning with Temporal Coding and Reward-Modulated Plasticity}}