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Section 1. Machine Learning in Fundamental Physics
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36. Реконструкция энергии космических лучей ультравысоких энергий, зарегистрированных флуоресцентным телескопом: одного такта времени может быть достаточно
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40. Фильтрация ложных максимумов ШАЛ с помощью нейросетевых методов в эксперименте СФЕРА-3
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71. Использование нейронного автокодировщика для генерации показаний поверхностных детекторов Telescope Array
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52. Графовая нейронная сеть с механизмом внимания для кластеризации треков частиц по событиям в эксперименте SPD на ускорителе NICA
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69. Machine Learning Approach for Lattice Quantum Field Theory Calculations
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75. Нейросетевое моделирование оптических солитонов, описываемых обобщённым нелинейным уравнением Шредингера шестого порядка с высокой нелинейностью
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65. Temporal difference modulated spiking actor learning
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41. SBI в задачах анализа динамических изображений многоканального детектора
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76. Simulation of trawl processes using SINN architectures
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39. ML-Based Optimum Sub-system Size Heuristic for the GPU Implementation of the Tridiagonal Partition Method
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44. Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models with Enhanced Nishimori Temperature Estimation
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43. Analysis of the TAIGA-HiSCORE Data using the Latent Space of Autoencoders
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81. Проблема аугментация данных атмосферных черенковских телескопов в стерео режиме на примере установки TAIGA-IACT
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42. Возможность применения метода нормализующих потоков для извлечения редких гамма событий в эксперименте TAIGA
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95. Гамма-астрономия ультравысоких энергий и проект TAIGA-100
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Section 2. Machine Learning for Environmental Sciences
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15. Восстановление приповерхностной влажности атмосферы над океаном с применением методов машинного обучения
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16. Сравнение моделей машинного обучения в задаче идентификации аномалий в данных визуальной съемки поверхности моря
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45. Application of Convolutional Neural Networks for Upper Ionosphere Remote Sensing Using All-Sky Camera Data
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54. Foundation models of ocean and atmosphere in 2025: milestones and perspectives.
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60. Сравнение моделей машинного обучения в задаче идентификации аномалий в данных визуальной съемки поверхности моря
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56. Detection of Irminger Rings in high resolution ocean hydrodynamic modeling data using artificial neural networks
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73. Моделирование турбулентного переноса примесей в планетарном пограничном слое с применением методов крупных вихрей и методов машинного обучения
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51. Нейросетевое пространственное масштабирование полей приповерхностного ветра над Баренцевым и Карским морями
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82. Сравнение методов машинного обучения для учета связей с запаздыванием при моделировании городского острова тепла.
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72. Deep Learning-Based Estimation of wind induced waves parameters from X-Band Radar Imagery
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83. USING MACHINE LEARNING METHODS FOR JOINT PROCESSING OF DATA FROM MULTIPLE SEMICONDUCTOR GAS SENSORS
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67. Доменная адаптация нейронных сетей в задаче диагностики природных вод по спектрам комбинационного рассеяния света
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89. Восстановление высоты зданий с использованием машинного обучения и цифровой модели поверхности ArcticDEM
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92. Intercomparison of machine learning approaches for identifying hail from basic weather parameters
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55. Enhancing the Quality of Kp Index Machine Learning Forecasting Using Higher-Frequency Data and Feature Transformations
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96. Машинное обучение для статистической детализации характеристик пространственного распределения осадков в Московском регионе
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Section 3. Machine Learning in Natural Sciences
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37. Neutron spectrum unfolding with deep learning models for tabular data
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49. Применение концепции переноса обучения для градиентного бустинга при решении обратных задач разведочной геофизики
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48. Анализ стратегий обучения FBPINNs
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62. The creation of reasonable robot control behavior in the form of executable code
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63. Применение сетей Колмогорова-Арнольда для решения обратной задачи спектроскопии при создании мультимодального наносенсора ионов металлов на основе углеродных точек
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64. Optimization of IRT-T research reactor fuel loading pattern by genetic algorithm
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59. Камни: Коллективная игра между агентами разнообразных типов, разработанная для изучения взаимодействия человека и искусственного интеллекта в многоагентной среде
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70. Построение нейродифференциальных уравнений с применением методов обратных задач динамики
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74. СОЗДАНИЕ ДИНАМИЧЕСКОГО КОГНОВИЗОРА – РАСПОЗНАВАНИЕ КОГНИТИВНЫХ СОСТОЯНИЙ С ПОМОЩЬЮ МЕТОДОВ ГЛУБОКОГО ОБУЧЕНИЯ
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78. Analysis of PINN Training Strategies for Heat Conduction Problems
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66. Comparison of Data Generation Methods for Spectral Analysis Using Variational Autoencoders
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87. Применение переноса обучения сверточной нейронной сети для повышения точности решения обратной задачи фотолюминесцентной наносенсорики
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86. Probabilistic Spiking Neural Network with Correlation-based Memristive Synaptic Updates
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90. Модель машинного обучения для прогнозирования вентиляторных порогов
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85. Finding optimal carbon dots synthesis parameters for quantitative analysis of components in multi-component aqueous solutions using machine learning
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91. Classifying Russian speech commands with a hardware-deployable spiking neural network transferred from an artificial neural network
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