Moscow University Physics Bulletin最新文献

{"title":"Encoding of Input Signals in Terms of Path Complexes in Spiking Neural Networks","authors":"V. A. Ilyin,&nbsp;Ya. P. Ivina,&nbsp;M. Yu. Khristichenko,&nbsp;A. V. Serenko,&nbsp;R. B. Rybka","doi":"10.3103/S0027134924702084","DOIUrl":"10.3103/S0027134924702084","url":null,"abstract":"<p>The article proposes a method for encoding input signals in a spiking neural network based on the mathematics of path complexes on directed graphs. The hypothesis formulated is that when the input signal is repeatedly applied, the STDP dynamics increases synaptic weights along the pathways of active neurons, while the weights of other synaptic connections decrease. As a result, a directed subgraph (path complex) is appearing for each input signal consisting of edges with large synaptic weights. Such path complexes should be unique for different input signals. This hypothesis is confirmed by the example of a simple spiking neural network model, for which a relevant parameter window has been found. Two methods of comparing path complexes (input signals encodings) are proposed. The first one is based on the introduction of the Euclidean metric on a set of path complexes, and the computation of distances between path complexes. The second one consists of compiling the algebra-topological portraits of path complexes—simplexes and homologies, and their subsequent comparison. The proposed method of encoding input signals is, in fact, a new tool that can be considered as an initial stage in the development of a new type of approaches to data analysis.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S630 - S638"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pointwise and Complex Quality Metrics in Atmospheric Modeling: Methods and Approaches","authors":"V. Yu. Rezvov,&nbsp;M. A. Krinitskiy,&nbsp;M. A. Borisov","doi":"10.3103/S0027134924702229","DOIUrl":"10.3103/S0027134924702229","url":null,"abstract":"<p>In atmospheric sciences, various quantitative indicators, or metrics, are used to describe the quality of modeling results of various flavors including numerical weather prediction, statistical correction, various downscaling products, etc. Metrics provide the accuracy of reproduction of the processes underlying the models and allow comparison of models by assessing the uncertainty of their results. The key importance of metrics lies in a more thorough study of the advantages and disadvantages of classical approaches and in the development of new, more complex assessment methods. This article presents a classification of the most frequently encountered quality metrics in the scientific literature. In addition to assessing traditional pointwise metrics, complex methods considering various aspects of modeling results and special metrics used in climate studies are described. Among the complex metrics, methods with an emphasis on the spatial structure and heterogeneity of the predicted variable fields and probabilistic methods for verifying ensemble forecasts are distinguished. Special attention in this paper is devoted to the growing popularity of object-oriented metrics and metrics based on rare and extreme events. Climate models are assessed by comparing the results of retrospective modeling with historical data, which complicates the choice of metrics. A variety of climate metrics focusing on specific climate processes or integrating several parameters is described. The need for developing more diverse metrics for effective evaluation of climate models is explored. All metrics considered in this article are supplemented by examples in the scientific literature and assessments of their application to atmospheric research.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S750 - S764"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature Selection Methods for Deep Learning Models of Soft Sensors in Oil Refining","authors":"I. S. Lazukhin,&nbsp;M. I. Petrovskiy,&nbsp;I. V. Mashechkin","doi":"10.3103/S0027134924702333","DOIUrl":"10.3103/S0027134924702333","url":null,"abstract":"<p>The development of automated control systems results into industrial plants accumulating large amounts of data on the continuous state of technological processes. Multiple physical sensors record the system states at any given time, hence being crucially responsible for controlling the system and maintaining its parameters within hard limits. At the same time, irregularly conducted laboratory measures make up a significant part of the qualitative indicators of such processes, especially in the petrochemical industry. Mathematical models that generalize laboratory measured indicators to match the frequency of physical sensors are called soft sensors. On practice, soft sensors for laboratory data are represented by linear or last-recorded-value models. We investigate the task of analytically obtaining chemical indicators of the technological process in real time based on the values of physical sensors; the study is conducted on a real-world data set. Several problems are covered, including high dimension of the physical inputs compared to the laboratory data volume; scarcity of the laboratory data collected on a daily basis. Authors propose feature selection methods based on PLS regression (hierarchical clustering), Bayes trees, utilize existing graph neural network, as well as compare developed methods with existing popular approaches. Each of the proposed feature selection methods has been adapted to take into account the expert opinion of the industrial plant engineers. Authors investigate developed approaches alongside neural network methods for predicting time series including graph neural networks, fully connected and recurrent networks. The obtained experimental results show the advantage of using proposed feature selection based on PLS and Bayes in ensemble with simple recurrent networks or graph neural networks with preliminary interpolation. Separately, it is worth noting the ambiguity of assessing the developed models quality; authors propose a combined approach that takes into account the adequacy of the model, its correlation with the true laboratory values and averaged errors.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S872 - S889"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solving Problems of Mathematical Physics on Radial Basis Function Networks","authors":"D. A. Stenkin,&nbsp;V. I. Gorbachenko","doi":"10.3103/S0027134924702163","DOIUrl":"10.3103/S0027134924702163","url":null,"abstract":"<p>The solution of boundary value problems described by partial differential equations on physics-informed neural networks is considered. Radial basis function networks are proposed as physics-informed neural networks. Such are easier to train compared to the fully connected networks usually used as physics-informed neural networks. An algorithm for solving the system of partial differential equations for the hydrodynamics problem is developed. On the example of the model problem of Kovasznay flow, programs for solving two-dimensional stationary Navier–Stokes equations using physics-informed radial basis function networks trained by the Nesterov method are developed.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S706 - S711"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating EAS Directions from TAIGA HiSCORE Data Using Fully Connected Neural Networks","authors":"A. P. Kryukov,&nbsp;S. P. Polyakov,&nbsp;Yu. Yu. Dubenskaya,&nbsp;E. O. Gres,&nbsp;E. B. Postnikov,&nbsp;P. A. Volchugov,&nbsp;D. P. Zhurov","doi":"10.3103/S0027134924702199","DOIUrl":"10.3103/S0027134924702199","url":null,"abstract":"<p>The TAIGA-HiSCORE setup is a wide-angle Cherenkov detector array for recording extensive air showers (EASs). The array comprises over 120 stations located in the Tunka Valley near Lake Baikal. One of the main tasks of data analysis in the TAIGA-HiSCORE experiment is to determine the axis direction of the EASs and their core location. These parameters are used to determine the source of gamma rays and play an important role in estimating the energy of the primary particle. The data collected by HiSCORE stations include signal amplitude and arrival time and allow for estimation of the shower direction of arrival. In this work, we use Monte Carlo simulation data for HiSCORE to demonstrate the feasibility of determining the EAS axis directions with artificial neural networks. Our approach employs multilayer perceptrons with skip connections, which take data from subsets of HiSCORE stations as input. Multiple station subsets are selected to derive more accurate composite estimates. Furthermore, we use a two-stage algorithm, where the initial direction estimates in the first stage are refined in the second stage. The final estimates have an average error of less than 0.25<span>({}^{circ})</span>. We plan to use this approach as a part of multimodal analysis of data obtained from several types of detectors used in the TAIGA experiment.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S724 - S730"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural Network Modeling of Optical Solitons Described by Generalized Nonlinear Schrödinger Equations","authors":"S. V. Zavertyaev,&nbsp;I. A. Moloshnikov,&nbsp;A. G. Sboev,&nbsp;M. S. Kuvakin","doi":"10.3103/S0027134924702114","DOIUrl":"10.3103/S0027134924702114","url":null,"abstract":"<p>The paper examines the modeling of pulse propagation in a nonlinear medium using two partial differential equations, namely the second-order Schrödinger equation and the fourth-order generalized nonlinear Schrödinger equation (GNSE). The applicability of physics-informed neural networks (PINN) methods for solving the GNSE is demonstrated for analyzing physical effects involving solitons, using the example of soliton interaction with an isolated wave. A study of the efficiency of balancing methods for the GNSE is conducted on a boundary value problem with zero boundary conditions, as well as an assessment of the accuracy of the PINN method with segmentation by comparing it to the exact solution for single solitons of the second and fourth-order GNSE. The use of conservation laws as a means of verifying the validity of the solution is experimentally justified, in the absence of the possibility of comparing it to an exact solution, thereby suggesting their use as an additional validation metric for the obtained solutions.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S666 - S675"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Gaussian Process Based Prediction of Density Distribution in Core of Research Nuclear Reactor","authors":"N. V. Smolnikov,&nbsp;M. N. Anikin,&nbsp;A. G. Naimushin,&nbsp;I. I. Lebedev","doi":"10.3103/S0027134924702394","DOIUrl":"10.3103/S0027134924702394","url":null,"abstract":"<p>Research nuclear reactors operate in a partial refueling mode, which leads to the formation of local areas with high nonuniformity of power density distribution. Such areas impact the economic efficiency of fuel consumption and the reactor core reliability. This necessitates the power density distribution profiling and underscores the importance of identifying the patterns of power distribution formation within the heterogeneous structure of the reactor core. In this study, an analysis of the reactor’s operational experience under various fuel loadings was conducted, and the characteristics of power density distribution in each cell were determined. An approach to applying a machine learning model for predicting power density distribution nonuniformity across the fuel cells of the IRT-T reactor core is presented. It is shown that the application of the supervised learning concept and Gaussian process regression with combined covariance (kernel) function enables the prediction of power distribution parameters in each reactor cell, regardless of the specific loading pattern and fuel burnup depth. The model achieved an overall accuracy of over 99<span>(%)</span>, with a mean absolute error not exceeding 0.5<span>(%)</span>.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S935 - S943"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spiking Neural Network Actor–Critic Reinforcement Learning with Temporal Coding and Reward-Modulated Plasticity","authors":"D. S. Vlasov,&nbsp;R. B. Rybka,&nbsp;A. V. Serenko,&nbsp;A. G. Sboev","doi":"10.3103/S0027134924702400","DOIUrl":"10.3103/S0027134924702400","url":null,"abstract":"<p>The article presents an algorithm for adjusting the weights of the spike neural network of the actor–critic architecture. A feature of the algorithm is the use of time coding of input data. The critic neuron is applied to calculate the change in the expected value of the action performed based on the difference in spike times received by the critic when processing the previous and current states. The change in the weights of the synaptic connections of the actor and critic neurons is carried out under the influence of local plasticity (spike–timing-dependent plasticity), in which the change in weight depends on the received value of the expected reward. The proposed learning algorithm was tested to solve the problem of holding a cart pole, in which it demonstrated its effectiveness. The proposed algorithm is an important step towards the implementation of reinforcement learning algorithms for spiking neural networks on neuromorphic computing devices.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S944 - S952"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstruction of Energy and Arrival Directions of UHECRs Registered by Fluorescence Telescopes with Neural Networks","authors":"Mikhail Zotov,&nbsp;for the JEM-EUSO Collaboration","doi":"10.3103/S0027134924702187","DOIUrl":"10.3103/S0027134924702187","url":null,"abstract":"<p>Fluorescence telescopes are important instruments widely used in modern experiments for registering ultraviolet radiation from extensive air showers (EASs) generated by cosmic rays of ultrahigh energies. We present proof-of-concept convolutional neural networks aimed at reconstruction of energy and arrival directions of primary particles using model data for two telescopes developed by the international JEM-EUSO collaboration. We also demonstrate how a simple convolutional encoder-decoder can be used for EAS track recognition. The approach is generic and can be adopted for other fluorescence telescopes.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S712 - S723"},"PeriodicalIF":0.4,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of Domain Wall Dynamics in Transparent Ferromagnets Using High-Speed Photography","authors":"T. B. Shapaeva","doi":"10.3103/S0027134924702515","DOIUrl":"10.3103/S0027134924702515","url":null,"abstract":"<p>The work is devoted to the investigation of the dynamics of domain walls and magnetic vortices arising within the domain walls of transparent ferromagnets. Initially, a review of methods for studying magnetization reversal dynamics is provided. Among the variety of these methods, high-speed photography based on the Faraday effect was selected for a more detailed consideration, since it allows for observing dynamic domain structures and determining the domain wall velocity with high accuracy. To optimize the use of the selected method, the study describes experimental investigations of magnetization reversal dynamics in materials with a high magneto-optical quality factor: Bi-containing ferrite—garnet films, GdFeCo, and yttrium orthoferrite. The choice of these materials is due to the fact that they exhibit high velocities of domain walls and magnetic vorteces arising in them, reaching up to 1.2 km/s in GdFeCo, approximately 10 km/s in garnet ferrites, and up to 20 km/s in yttrium orthoferrite. Additionally, ferrite garnets exhibit a periodic labyrinthine domain structure, enabling the use of magneto-optical diffraction to study the domain wall dynamics with high spatial resolution.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 6","pages":"813 - 838"},"PeriodicalIF":0.4,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}