Справки о публикации статей

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  2. S. Ali et al. «Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning»
  3. Yu.Yu. Dubenskaya et al. «Image Data Augmentation for the TAIGA-IACT Experiment with Conditional Generative Adversarial Networks»
  4. A.V. Golda et al. «Machine learning approach in the prediction of differential cross sections and structure functions of single pion electroproduction in the resonance region»
  5. E.O. Gres et al. «Gamma/hadron separation in the TAIGA experiment with neural network methods»
  6. V.A. Ilyin et al. «Encoding of input signals in terms of path complexes in spiking neural networks»
  7. D.V. Salnikov et al. «Application of Neural Networks for Path Integrals Computation in Relativistic Quantum Mechanics»
  8. V.S. Usatyuk et al. «Enhanced Image Clustering with Random- Bond Ising Models Using LDPC Graph Representations and Nishimori Temperature»
  9. S.V. Zavertyaev et al. «Network Modeling of Optical Solitons Described by Generalized Nonlinear Schrödinger Equations»
  10. E.L. Entina et al. «Application of convolutional neural networks for extensive air shower separation in the SPHERE-3 experiment»
  11. R.R. Fitagdinov et al. «Generation of grid surface detector data in the Telescope Array experiment using neural networks»
  12. A.P. Kryukov et al. «Machine Learning in Gamma Astronomy»
  13. E.O. Kurbatov et al. «Multidimensional global optimization of detector systems using the example of muon shield in the SHiP experiment»
  14. D.A. Stenkin et al. «Solving problems of mathematical physics on radial basis function networks»
  15. D. S. Zagorulia et al. «Morphological Classification of Jets in Active Galactic Nuclei»
  16. Mikhail Zotov et al. «Reconstruction of energy and arrival directions of UHECRs registered by fluorescence telescopes with neural networks»
  17. A.P. Kryukov et al. «Evaluating EAS Directions from TAIGA HiSCORE Data Using Fully Connectedv Neural Networks»
  18. I.V. Isaev et al. «Identification of Air Pollutants with Thermally Modulated Metal Oxide Semiconductor Gas Sensors through Machine Learning Based Response Models»
  19. M.A. Krinitsky et al. «An overview of machine learning and deep learning applications in Earth sciences in 2024: achievements and perspectives»
  20. V.Yu. Rezvov et al. «Pointwise and complex quality metrics in atmospheric modeling: methods and approaches»
  21. S.A. Sharakin et al. «Probabilistic programming methods for reconstruction of multichannel imaging detector events: ELVES and TRACKS»
  22. A.I. Suslov et al. «Machine learning methods for statistical prediction of PM2.5 in urban agglomerations with complex terrain, using Grenoble as an example»
  23. M.I. Varentsov et al. «Approximation of spatial and temporal variability of the urban heat island in Moscow using machine learning»
  24. R.D. Vladimirov et al. «Forecasting the state of the Earth's magnetosphere using a special algorithm for working with multidimensional time series»
  25. A.V. Vorobev et al. «Diagnostics of geoinduced currents in high latitude power systems using machine learning methods»
  26. Abdalaziz Al-Maeeni et al. «Engineering Point Defects in MoS2 for Tailored Material Properties using Large Language Models»
  27. A.N. Balandina et al. «“transformer” architecture for risk analysis of group effects of food nutrients»
  28. G.N. Chugreeva et al. «Development of a multimodal photoluminescent carbon nanosensor for metal ions in water using artificial neural networks»
  29. I.M. Gadzhiev, et al. «Comparative Analysis of the Procedures to Forecast the Kp Geomagnetic Index by Machine Learning»
  30. H.E. Karlinski et al. «Prediction of defect structure in MoS_2 by given properties»
  31. I.S. Lazukhin et al. «Feature selection methods for deep learning models of soft sensors in oil refining»
  32. A.I. Saevskiy et al. «An original algorithm for classifying premotor potentials in electroencephalogram signal for neurorehabilitation using a closed-loop brain-computer interface»
  33. N.O. Shchurov, et al. «Nonlinear relevance estimation of multicollinear features for reducing the input dimensionality of optical spectroscopy inverse problem»
  34. F. Shipilov et al. «Machine Learning for FARICH Reconstruction at NICA SPD»
  35. S.G. Shorokhov et al. «Improving Physics-Informed Neural Networks via Quasiclassical Loss Functionals»
  36. D. Sirota et al. «Neural Operators for Hydrodynamic Modeling of Underground Gas Storages»
  37. N.V. Smolnikov et al. «Gaussian process based prediction of density distribution in core of research nuclear reactor»
  38. D.S.Vlasov et al. «Spiking neural network actor-critic reinforcement learning with temporal coding and reward-modulated plasticity»