PoS - Proceedings of Science

The 6th International Workshop on Deep Learning in Computational Physics

6-8July 2022
JINR, Dubna, Russia

The Workshop will be held in the Meshcheryakov Laboratory of Information Technologies (MLIT) of the Joint Institute for Nuclear Research (JINR) on July 6-8, 2022.

The workshop primarily focuses on the use of machine learning in particle astrophysics and high energy physics, but is not limited to this area. Topics of interest are various applications of artificial neural networks to physical problems, as well as the development of new modern machine learning methods for analyzing various scientific data, including big data.

The workshop website: https://dlcp2022.jinr.ru/.

 * Joint Institute for Nuclear Research, Meshcheryakov Laboratory of Information Technologies (MLIT JINR, Dubna, Russia) 
 * M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of Nuclear Physics (SINP MSU, Moscow, Russia).

Track 1. Machine Learning in Particle Astrophysics and High Energy Physics

 * Machine learning methods in particle astrophysics and high energy physics.
 * Fast event generators based on machine learning for modelling of physics
 * Multi-messenger data analysis of experimental data.
 * Application machine learning for data analysis in LHC, NICA, TAIGA and
other experimental facilities.

Track 2. Modern Machine Learning Methods

 * Convolutional neural networks.
 * Recurrent neural networks.
 * Graph neural networks.
 * Modern trends in machine learning.

Track 3. Machine Learning in Natural Sciences

 * Biology and bioinformatics.
 * Engineering sciences.
 * Climate prediction and Earth monitoring.

Track 4. Machine Learning in Education

 * Machine learning in High education.
 * Outreach knowledge in machine learning.

conference main image
Track1. Machine Learning in Particle Astrophysics and High Energy Physics
Track2. Modern Machine Learning Methods
Track3. Machine Learning in Natural Sciences
Track4. Machine Learning in Education
PoS(DLCP2022)032 pdf A. Kryukov and V. Korenkov
Track1. Machine Learning in Particle Astrophysics and High Energy Physics
A machine learning approach to identify the air shower cores for the GRAPES-3 experiment
PoS(DLCP2022)001 pdf
M. Chakraborty, S. Ahmad, A. Chandra, S.R. Dugad, U.D. Goswami, S.K. Gupta, B. Hariharan, Y. Hayashi, P. Jagadeesan, A. Jain, P. Jain, S. Kawakami, H. Kojima, S. Mahapatra, P.K. Mohanty, R. Moharana, Y. Muraki, P.K. Nayak, T. Nonaka, A. Oshima, S. Paul, B.P. Pant, D. Pattanaik, G.S. Pradhan, M. Rameez, K. Ramesh, L.V. Reddy, R. Sahoo, R. Scaria, S. Shibata, K. Tanaka, F. Varsi and M. Zuberi
Energy reconstruction in analysis of Cherenkov telescopes images in TAIGA experiment using deep learning methods
PoS(DLCP2022)002 pdf E. Gres and A. Kryukov
Using conditional variational autoencoders to generate images from atmospheric Cherenkov telescopes
PoS(DLCP2022)003 pdf S. Polyakov, A. Kryukov, A. Demichev, J. Dubenskaya, E. Gres and A.A. Vlaskina
Using a Conditional Generative Adversarial Network to Control the Statistical Characteristics of Generated Images for IACT Data Analysis
PoS(DLCP2022)004 pdf J. Dubenskaya, A. Kryukov, A. Demichev, S. Polyakov, E. Gres and A.A. Vlaskina
Deep neural network applications for particle tracking at the BM@N and SPD experiments
PoS(DLCP2022)005 pdf D. Rusov, A. Nikolskaia, P.V. Goncharov, E. Shchavelev and G. Ososkov
Application of convolutional neural networks for data analysis in TAIGA-HiSCORE experiment
PoS(DLCP2022)006 pdf A.A. Vlaskina and A. Kryukov
Track2. Modern Machine Learning Methods
Decomposition of Spectral Contour into Gaussian Bands using Gender Genetic Algorithm
PoS(DLCP2022)009 pdf G. Kupriyanov, I. Isaev, I. Plastinin, T. Dolenko and S. Dolenko
A spiking neural network with fixed synaptic weights based on logistic maps for a classification task
PoS(DLCP2022)010 pdf A. Sboev, D. Kunitsyn, A. Serenko and R. Rybka
Stochastic vs. BFGS Training in Neural Discrimination of RF-Modulation
PoS(DLCP2022)011 pdf M. Dima, M.T. Dima and M. Mihailescu
Self-organized intelligent quantum controller: quantum deep learning and quantum genetic algorithm – QSCOptKBTM toolkit
PoS(DLCP2022)012 pdf D.P. Zrelova, V. Korenkov, A. Reshetnikov, S. Ulyanov and P. Zrelov
Deep learning approach to high dimensional problems of quantum mechanics
PoS(DLCP2022)013 pdf V.A. Roudnev and M. Stepanova
Relation Extraction from Texts Containing Pharmacologically Significant Information on base of Multilingual Language Models
PoS(DLCP2022)014 pdf A.A. Selivanov, A. Gryaznov, R. Rybka, A. Sboev, S. Sboeva and Y. Klueva
Track3. Machine Learning in Natural Sciences
Short-length peptides contact map prediction using Convolution Neural Networks
PoS(DLCP2022)016 pdf A.D. Maminov
Application of a neural network approach to the task of arena marking for the ”Open Field” behavioral test
PoS(DLCP2022)017 pdf A.I. Anikina, D. Podgainy, A. Stadnik, O. Streltsova, I. Kolesnikova, Y. Severiukhin and D. Savvateev
Neural network recovery of missing data of one geophysical method from known data of another one in solving inverse problems of exploration geophysics
PoS(DLCP2022)018 pdf I. Isaev, I. Obornev, E. Obornev, E. Rodionov, M. Shimelevich and S. Dolenko
Hazy images dataset with localized light sources for experimental evaluation of dehazing methods
PoS(DLCP2022)019 pdf A.I. Filin, A.V. Kopylov, O. Seredin and I. Gracheva
Visual clustering of marine sediment particles using a combination of unsupervised machine learning methods
PoS(DLCP2022)020 pdf V.A. Golikov, M. Krinitskiy and D. Borisov
Google Earth Engine and machine learning for Earth monitoring
PoS(DLCP2022)021 pdf attachments A.V. Uzhinskiy
Data-driven approximation of downward solar radiation flux based on all-sky optical imagery using machine learning models trained on DASIO dataset
PoS(DLCP2022)022 pdf V.S. Koshkina, M. Krinitskiy, N. Anikin, M. Borisov and S. Gulev
Approximation of high-resolution surface wind speed in the North Atlantic using discriminative and generative neural models based on RAS-NAAD 40-year hindcast
PoS(DLCP2022)023 pdf V.Y. Rezvov, M. Krinitskiy and S. Gulev
Underwater biotope mapping: automatic processing of underwater video data
PoS(DLCP2022)024 pdf O.O. Iakushkin, E. Pavlova, A. Lavrova, E. Pen, O. Sedova, V. Polovkov, N. Shabalin, T. Yana and F.H. Anna
Accuracy of COVID-19 evolution models for different forecast horizons
PoS(DLCP2022)025 pdf S. Zavertyaev, I. Moloshnikov, A. Naumov, R. Rybka and A. Sboev
Taking into Account Mutual Correlations during Selection of Significant Input Features in Neural Network Solution of Inverse Problems of Spectroscopy
PoS(DLCP2022)026 pdf N. Shchurov, I. Isaev, S. Burikov, T. Dolenko, K. Laptinskiy and S. Dolenko
Track4. Machine Learning in Education
ML/DL/HPC Ecosystem of the HybriLIT Heterogeneous Platform (MLIT JINR): New Opportunities for Applied Research
PoS(DLCP2022)027 pdf M.I. Zuev, Y. Butenko, M. Ćosić, A. Nechaevskiy, D. Podgainy, I. Rahmonov, A. Stadnik and O. Streltsova
Methods and algorithms of the analytical platform for analyzing the labor market and the compliance of the higher education system with market needs
PoS(DLCP2022)028 pdf A.V. Ilina, S. Belov, I. Filozova, Y. Gavrilenko, J. Javadzade, I. Kadochnikov, V. Korenkov, I. Pelevanyuk, D. Priakhina, R. Semenov, V. Tarabrin and P. Zrelov
Neuromorphic improvement of the Weizsäecker formula
PoS(DLCP2022)029 pdf M.O. Dima
NARX neural prediction of oscillationalinstability at the IBR-2M reactor
PoS(DLCP2022)031 pdf M.T. Dima, M. Dima and M. Mihailescu

When the link to the pdf file is not available, the contribution in question has not yet been accepted for publication.