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-====== Справки о публикации статей ======
-  - {{ :dlcp2024:letters:справки_1.pdf | E.E. Abasov et al. «Application of Kolmogorov-Arnold Networks
-in high energy physics»}}
-  - {{ :dlcp2024:letters:справки_2.pdf | S. Ali et al. «Calibrating for the Future: Enhancing Calorimeter
-Longevity with Deep Learning»}}
-  - {{ :dlcp2024:letters:справки_3.pdf | Yu.Yu. Dubenskaya et al. «Image Data Augmentation for the
-TAIGA-IACT Experiment with Conditional Generative Adversarial Networks»}}
-  - {{ :dlcp2024:letters:справки_4.pdf | 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»}}
-  - {{ :dlcp2024:letters:справки_5.pdf | E.O. Gres et al. «Gamma/hadron separation in the TAIGA
-experiment with neural network methods»}}
-  - {{ :dlcp2024:letters:справки_6.pdf | V.A. Ilyin et al. «Encoding of input signals in terms of path
-complexes in spiking neural networks»}}
-  - {{ :dlcp2024:letters:справки_7.pdf | D.V. Salnikov et al. «Application of Neural Networks for Path
-Integrals Computation in Relativistic Quantum Mechanics»}}
-  - {{ :dlcp2024:letters:справки_8.pdf | V.S. Usatyuk et al. «Enhanced Image Clustering with Random-
-Bond Ising Models Using LDPC Graph Representations and Nishimori Temperature»}}
-  - {{ :dlcp2024:letters:справки_9.pdf | S.V. Zavertyaev et al. «Network Modeling of Optical Solitons
-Described by Generalized Nonlinear Schrödinger Equations»}}
-  - {{ :dlcp2024:letters:справки_10.pdf | E.L. Entina et al. «Application of convolutional neural networks
-for extensive air shower separation in the SPHERE-3 experiment»}}
-  - {{ :dlcp2024:letters:справки_11.pdf | R.R. Fitagdinov et al. «Generation of grid surface detector data in the Telescope Array experiment using neural networks»}}
-  - {{ :dlcp2024:letters:справки_12.pdf | A.P. Kryukov et al. «Machine Learning in Gamma Astronomy»}}
-  - {{ :dlcp2024:letters:справки_13.pdf | E.O. Kurbatov et al. «Multidimensional global optimization of
-detector systems using the example of muon shield in the SHiP experiment»}}
-  - {{ :dlcp2024:letters:справки_14.pdf | D.A. Stenkin et al. «Solving problems of mathematical physics on
-radial basis function networks»}}
-  - {{ :dlcp2024:letters:справки_15.pdf | D. S. Zagorulia et al. «Morphological Classification of Jets in
-Active Galactic Nuclei»}}
-  - {{ :dlcp2024:letters:справки_16.pdf | Mikhail Zotov et al. «Reconstruction of energy and arrival
-directions of UHECRs registered by fluorescence telescopes with neural networks»}}
-  - {{ :dlcp2024:letters:справки_17.pdf | A.P. Kryukov et al. «Evaluating EAS Directions from TAIGA
-HiSCORE Data Using Fully Connectedv Neural Networks»}}
-  - {{ :dlcp2024:letters:справки_18.pdf | I.V. Isaev et al. «Identification of Air Pollutants with Thermally
-Modulated Metal Oxide Semiconductor Gas Sensors through Machine Learning Based Response Models»}}
-  - {{ :dlcp2024:letters:справки_19.pdf | M.A. Krinitsky et al. «An overview of machine learning and deep
-learning applications in Earth sciences in 2024: achievements and perspectives»}}
-  - {{ :dlcp2024:letters:справки_20.pdf | V.Yu. Rezvov et al. «Pointwise and complex quality metrics in
-atmospheric modeling: methods and approaches»}}
-  - {{ :dlcp2024:letters:справки_21.pdf | S.A. Sharakin et al. «Probabilistic programming methods for
-reconstruction of multichannel imaging detector events: ELVES and TRACKS»}}
-  - {{ :dlcp2024:letters:справки_22.pdf | 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»}}
-  - {{ :dlcp2024:letters:справки_23.pdf | M.I. Varentsov et al. «Approximation of spatial and temporal
-variability of the urban heat island in Moscow using machine learning»}}
-  - {{ :dlcp2024:letters:справки_24.pdf | R.D. Vladimirov et al. «Forecasting the state of the Earth's
-magnetosphere using a special algorithm for working with multidimensional time series»}}
-  - {{ :dlcp2024:letters:справки_25.pdf | A.V. Vorobev et al. «Diagnostics of geoinduced currents in high
-latitude power systems using machine learning methods»}}
-  - {{ :dlcp2024:letters:справки_26.pdf | Abdalaziz Al-Maeeni et al. «Engineering Point Defects in MoS2
-for Tailored Material Properties using Large Language Models»}}
-  - {{ :dlcp2024:letters:справки_27.pdf | A.N. Balandina et al. «“transformer” architecture for risk analysis of group effects of food nutrients»}}
-  - {{ :dlcp2024:letters:справки_28.pdf | G.N. Chugreeva et al. «Development of a multimodal
-photoluminescent carbon nanosensor for metal ions in water using artificial neural networks»}}
-  - {{ :dlcp2024:letters:справки_29.pdf | I.M. Gadzhiev, et al. «Comparative Analysis of the Procedures to
-Forecast the Kp Geomagnetic Index by Machine Learning»}}
-  - {{ :dlcp2024:letters:справки_30.pdf | H.E. Karlinski et al. «Prediction of defect structure in
-MoS_2 by given properties»}}
-  - {{ :dlcp2024:letters:справки_31.pdf | I.S. Lazukhin et al. «Feature selection methods for deep learning
-models of soft sensors in oil refining»}}
-  - {{ :dlcp2024:letters:справки_32.pdf | A.I. Saevskiy et al. «An original algorithm for classifying
-premotor potentials in electroencephalogram signal for neurorehabilitation using a closed-loop brain-computer interface»}}
-  - {{ :dlcp2024:letters:справки_33.pdf | N.O. Shchurov, et al. «Nonlinear relevance estimation of
-multicollinear features for reducing the input dimensionality of optical spectroscopy inverse problem»}}
-  - {{ :dlcp2024:letters:справки_34.pdf | F. Shipilov et al. «Machine Learning for FARICH Reconstruction
-at NICA SPD»}}
-  - {{ :dlcp2024:letters:справки_35.pdf | S.G. Shorokhov et al. «Improving Physics-Informed Neural
-Networks via Quasiclassical Loss Functionals»}}
-  - {{ :dlcp2024:letters:справки_36.pdf | D. Sirota et al. «Neural Operators for Hydrodynamic Modeling of
-Underground Gas Storages»}}
-  - {{ :dlcp2024:letters:справки_37.pdf | N.V. Smolnikov et al. «Gaussian process based prediction of
-density distribution in core of research nuclear reactor»}}
-  - {{ :dlcp2024:letters:справки_38.pdf |D.S.Vlasov et al. «Spiking neural network actor-critic
-reinforcement learning with temporal coding and reward-modulated plasticity»}}