Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)最新文献

{"title":"Application of a neural network approach to the task of arena marking for the ”Open Field” behavioral test","authors":"A. Anikina, D. Podgainy, A. Stadnik, O. Streltsova, I. Kolesnikova, Yurii Severiukhin, Dmitry Savvateev","doi":"10.22323/1.429.0017","DOIUrl":"https://doi.org/10.22323/1.429.0017","url":null,"abstract":"","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"35 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125099016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ML/DL/HPC Ecosystem of the HybriLIT Heterogeneous Platform (MLIT JINR): New Opportunities for Applied Research","authors":"M. Zuev, Y. Butenko, M. Ćosić, A. Nechaevskiy, D. Podgainy, I. Rahmonov, A. Stadnik, O. Streltsova","doi":"10.22323/1.429.0027","DOIUrl":"https://doi.org/10.22323/1.429.0027","url":null,"abstract":"","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134476053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-organized intelligent quantum controller: quantum deep learning and quantum genetic algorithm – QSCOptKBTM toolkit","authors":"Daria Zrelova, V. Korenkov, A. Reshetnikov, S. Ulyanov, P. Zrelov","doi":"10.22323/1.429.0012","DOIUrl":"https://doi.org/10.22323/1.429.0012","url":null,"abstract":"","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"10 35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132574680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methods and algorithms of the analytical platform for analyzing the labor market and the compliance of the higher education system with market needs","authors":"A. V. Ilina, S. Belov, I. Filozova, Y. Gavrilenko, J. Javadzade, I. Kadochnikov, V. Korenkov, I. Pelevanyuk, D. Priakhina, R. Semenov, V. Tarabrin, P. Zrelov","doi":"10.22323/1.429.0028","DOIUrl":"https://doi.org/10.22323/1.429.0028","url":null,"abstract":"","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121459024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural network recovery of missing data of one geophysical method from known data of another one in solving inverse problems of exploration geophysics","authors":"I. Isaev, I. Obornev, E. Obornev, E. Rodionov, M. Shimelevich, S. Dolenko","doi":"10.22323/1.429.0018","DOIUrl":"https://doi.org/10.22323/1.429.0018","url":null,"abstract":"This study is devoted to the inverse problems of exploration geophysics, which consist in reconstructing the spatial distribution of the properties of the medium in the Earth’s thickness from the geophysical fields measured on its surface. We consider the methods of gravimetry, magnetometry, and magnetotelluric sounding, as well as their integration, i.e. simultaneous use of data from several geophysical methods to solve the inverse problem. In their previous studies, the authors have shown that the integration of geophysical methods allows improving the quality of the solution of the inverse problem in comparison with the individual use of each of them. One of the obstacles to using the integration of geophysical methods can be the situation when for some measurement points there is no data from one of the geophysical methods used. At the same time, the data spaces of different integrated geophysical methods are interconnected, and the values of the observed quantities (fields) for one of the methods can be possibly recovered from the known values of the observed quantities of another geophysical method by constructing a preliminary adaptive mapping of one of the spaces to another. In this study, we investigate the neural network recovery of missing data of one geophysical method from the known data of another one and compare the quality of the solution of the","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123957853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Taking into Account Mutual Correlations during Selection of Significant Input Features in Neural Network Solution of Inverse Problems of Spectroscopy","authors":"N. O. Shchurov, I. Isaev, S. Burikov, T. Dolenko, K. Laptinskiy, S. Dolenko","doi":"10.22323/1.429.0026","DOIUrl":"https://doi.org/10.22323/1.429.0026","url":null,"abstract":"In the neural network solution of many physical problems, it becomes necessary to reduce the dimension of the input data in order to achieve a more accurate and stable solution while reducing computational complexity. When solving the inverse problem of spectroscopy, high multicollinearity between input features is often observed, as spectral lines may be much wider than the spectral channel width. This leads to the need to use a feature selection method that takes into account this characteristic. The method discussed in this article is based on iterative selection of input features with the highest Pearson correlation with the target variable and elimination of input features with high cross-correlation. This study compares the quality of the neural network solution to the problem of determining the concentration of heavy metal ions in water by Raman and absorption spectra on the full feature set and on its subsets produced by the considered feature selection method and by conventional methods of selection of significant input features.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130354821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven approximation of downward solar radiation flux based on all-sky optical imagery using machine learning models trained on DASIO dataset","authors":"M. Krinitskiy, Vasilisa Koshkina, N. Anikin, Mikhail Borisov, S. Gulev","doi":"10.22323/1.429.0022","DOIUrl":"https://doi.org/10.22323/1.429.0022","url":null,"abstract":"Cloud cover is the main physical factor limiting the downward shortwave (SW) solar radiation flux. In modern models of climate and weather forecasts, physical models describing radiative transfer through clouds may be used. However this option computationally expensive. Instead, one may use parameterizations which are simplified schemes for approximating environmental variables. The purpose of our study is to assess the capabilities of machine learning models of approximating radiation flux based on all-sky optical imagery in order to assess the links between observed cloud cover properties with the flux. We applied various machine learning (ML) models: classic ML models and convolutional neural networks (CNN). These models were trained using the dataset of all-sky optical imagery accompanied by SW radiation flux measurements. The Dataset of All-Sky Imagery over the Ocean (DASIO) is collected in Indian, Atlantic and Arctic oceans during several expeditions from 2014 till 2021. When training our CNN, we applied heavy source data augmentation in order to force the CNN to become invariant to brightness variations and, thus, approximating the relationship between the visual structure of clouds and SW flux. We demonstrate that the CNN supersedes existing parameterizations known from literature in terms of RMSE. Our results allow us to assume that one may acquire downward shortwave radiation flux directly from all-sky imagery. We also demonstrate that CCNs are capable of estimating downward SW radiation flux based on clouds’ visible structure. Our results suggest that there are outliers in DASIO dataset that may be filtered in forthcoming studies. The results also suggest that hyperparameters optimization of our CNN and ensemble models may help discovering better configurations including proper dataset re-weighting as well as","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114550671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decomposition of Spectral Contour into Gaussian Bands using Gender Genetic Algorithm","authors":"S. Dolenko, Gavriil Kupriyanov, I. Isaev, I. Plastinin, T. Dolenko","doi":"10.22323/1.429.0009","DOIUrl":"https://doi.org/10.22323/1.429.0009","url":null,"abstract":"One of the methods for analysis of complex spectral contours (especially for spectra of liquid objects) is their decomposition into a limited number of spectral bands with physically reasonable shapes (Gaussian, Lorentzian, Voigt etc.). The problem with the required decomposition is that such decomposition is an inverse problem that is often ill-conditioned or even incorrect, especially in presence of noise in spectra. Therefore, this problem is often solved by advanced optimization methods less subject to be stuck in local minima, such as genetic algorithms (GA). In the conventional version of GA, all individuals are similar regarding the probabilities and implementation of the main genetic operators (crossover and mutation) and the procedure of selection. In this study, we test a new version of GA – gender GA (GGA), where the individuals of the two genders differ by the probability of mutation (higher for the male gender) and by the procedures of selection for crossover. In this study, we compare the efficiency of gradient descent and conventional GA and GGA followed by gradient descent from the found point in solving the problems of decomposition of the Raman valence band of liquid water into Gaussian shaped components.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124944820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approximation of high-resolution surface wind speed in the North Atlantic using discriminative and generative neural models based on RAS-NAAD 40-year hindcast","authors":"M. Krinitskiy, Vadim Yuryevich Rezvov, S. Gulev","doi":"10.22323/1.429.0023","DOIUrl":"https://doi.org/10.22323/1.429.0023","url":null,"abstract":"Surface wind is one of the most important atmospheric fields in climate research. Accurate prediction of high spatial resolution surface wind has a wide variety of applications, such as renewable wind energy and forecasts of extreme weather events. General circulation models (GCMs) study climate system on a global scale. Their main issues are the low resolution of the modeling results and high computational costs. One of the solutions to these problems is statistical downscaling. Statistical downscaling methods discover functional relationships avoiding computationally expensive high-resolution hydrodynamic simulations. Deep learning methods, including artificial neural networks (ANNs), are one of the typical machine-learning approaches approximating complex nonlinear relationships. In our study, we explored the capabilities of statistical 5x spatial downscaling of surface wind over the ocean in the North Atlantic region. Low-resolution input data and high-resolution validation data were provided by RAS-NAAD 40-year hindcast. We applied several downscaling methods, including bicubic interpolation as a reference solution, various discriminative convolutional neural networks (CNNs) such as Linear CNN, Residual CNN, CNN with skip connections, and generative adversarial network (GAN) based on SR-GAN. We also compared downscaling results in terms of RMSE, PSNR and other quality metrics including the ones representing the reconstruction of extreme winds. We evaluated the computational costs and the quality of different methods and reference solution to identify advantages and lacks of machine-learning downscaling. As a result, both discriminative and generative ANN-based downscaling methods have not outperformed reference solution in downscaling quality. Nevertheless, for further research, we consider GANs as the most promising ANN architectures for surface wind downscaling based on their fine-structure modeling ability.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"461 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116777304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual clustering of marine sediment particles using a combination of unsupervised machine learning methods","authors":"M. Krinitskiy, V. Golikov, D. Borisov","doi":"10.22323/1.429.0020","DOIUrl":"https://doi.org/10.22323/1.429.0020","url":null,"abstract":"The information on the past climates or environments is preserved in natural archives, such as, for example, marine sediments covering the sea-floor. The study of sediment composition in coarse fraction (>0.063 mm) is widely used, yet time-consuming technique useful for recognizing ancient environments. The coarse fraction analysis is generally performed visually under binocular microscope and requires the high qualification of the observer. In this study, we propose a method to automate and accelerate this kind of work using a combination of classic computer vision and machine learning algorithms. Using an optical digital microscope with precise automatic positioning system, we photographed sieved and dried sediment samples composed of particles over 0.1 mm in size. We then applied a clustering pipeline including classical and neural machine learning techniques. We demonstrate that the proposed method is capable of dividing visual representations of marine sediment grains into homogeneous groups suitable for further accurate classification by an experienced specialist. Our method may significantly reduce the time costs of an expert conducting a study of marine sediments. This will allow further evaluation of sediment composition, main sediment sources and some important characteristics (proxies/indicators) marking a particular environmental setting in the past. The clustering results obtained using our algorithm may be used to train a more accurate classification algorithm.","PeriodicalId":262901,"journal":{"name":"Proceedings of The 6th International Workshop on Deep Learning in Computational Physics — PoS(DLCP2022)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114572341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}