DLCP2021
28-29 June, 2021
Moscow, Russia
published January 12, 2022
Entries on ADS

The workshop primarily focuses on the use of machine learning in cosmic-ray astrophysics, 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.

Organizers

• Karlsruhe Institute of Technology (Karlsruhe, Germany)

• D.V. Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University (Moscow, Russia)

• Matrosov Institute for System Dynamics and Control Theory SB RAS (Irkutsk, Russia)

The main topics

• Modern machine learning method in physics

• Deep learning in cosmic ray astrophysics

• Generative adversarial network for modelling of physics phenomena

• Multi-messenger data analysis in astroparticle physics

• Application in biology and other natural sciences

• Modern trends in machine learning

Editorial Board

  • Andrey Demichev
    Skobeltsyn Institute for Nuclear Physics Moscow State University
  • Andreas Haungs
    Karlsruhe Institute of Technology, Institute for Astroparticle Physics (IAP)
  • Vlacheslav Ilyin
    NRC Kurchatov Institute, Russia
  • Alexander Kryukov
    Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Russia
  • Alexey Shigarov
    Institute for System Dynamics and Control Theory, SB RAS, Russia
conference main image
Sessions
Editorial
Regular papers
Short papers
Editorial
The 5th International Workshop on Deep Learning in Computational Physics – DLCP-2021
A. Haungs and A. Kryukov
Regular papers
Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels
A. Demichev
Neural Network Solution of Inverse Problems of Geological Prospecting with Discrete Output
I. Isaev, I. Obornev, E. Obornev, E. Rodionov, M. Shimelevich and S. Dolenko
Graph Neural Networks and Application for Cosmic-Ray Analysis
P. Koundal
Artificial Neural Networks for the Identification of Partial Differential Equations of Land Surface Schemes in Climate Models
M. Krinitskiy, V. Stepanenko and R. Chernyshev
Evaluation of Machine Learning Methods for Relation Extraction Between Drug Adverse Effects and Medications in Russian Texts of Internet User Reviews
A. Sboev, A. Selivanov, R. Rybka, I. Moloshnikov and G. Rylkov
Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education
V. Tokareva, D. Kostunin, I. Plokhikh and V. Sotnikov
Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Machine Learning Methods
M. Vasyutina, L. Sveshnikova, I.I. Astapov, P.A. Bezyazeekov, M. Blank, E.A. Bonvech, A.N. Borodin, M. Brueckner, N.M. Budnev, A.V. Bulan, D.V. Chernov, A. Chiavassa, A.N. Dyachok, A.R. Gafarov, A.Y. Garmash, V.M. Grebenyuk, O.A. Gress, T.I. Gress, A.A. Grinyuk, O.G. Grishin, D. Horns, A.L. Ivanova, N.N. Kalmykov, V.V. Kindin, S.N. Kiryuhin, R.P. Kokoulin, K.G. Kompaniets, E.E. Korosteleva, V.A. Kozhin, E.A. Kravchenko, A.P. Kryukov, L.A. Kuzmichev, A.A. Lagutin, M.V. Lavrova, Y. Lemeshev, B.K. Lubsandorzhiev, N.B. Lubsandorzhiev, A.D. Lukanov, D. Lukyantsev, R.R. Mirgazov, R. Mirzoyan, R.D. Monkhoev, E.A. Osipova, A.L. Pakhorukov, L.A. Panasenko, A. Pan, L.V. Pankov, A.D. Panov, A.A. Petrukhin, D.A. Podgrudkov, V.A. Poleschuk, M. Popesku, E.G. Popova, A. Porelli, E.B. Postnikov, V.V. Prosin, V.S. Ptuskin, A.A. Pushnin, R.I.R. 𝑗, A. Razumov, E. Rjabov, G.I. Rubtsov, Y.I. Sagan, V.S. Samoliga, A. Sidorenkov, A.A. Silaev, A.A. Silaev jr, A.V. Skurikhin, M. Slunecka, A.V. Sokolov, Y. Suvorkin, V.A. Tabolenko, A.B. Tanaev, B.A. Tarashansky, M. Ternovoy, L.G. Tkachev, M. Tluczykont, N. Ushakov, A. Vaidyanathan, P.A. Volchugov, N.V. Volkov, D. Voronin, R. Wischnewski, I.I. Yashin, A.V. Zagorodnikov and D.P. Zhurov
Short papers
Legacy of Tunka-Rex Software and Data
P. Bezyazeekov, N.M. Budnev, O. Fedorov, O. Gress, O. Grishin, A. Haungs, T. Huege, Y. Kazarina, M. Kleifges, E. Korosteleva, D. Kostunin, L. Kuzmichev, V. Lenok, N. Lubsandorzhiev, S. Malakhov, T. Marshalkina, R. Monkhoev, E. Osipova, A. Pakhorukov, L. Pankov, V. Prosin, D. Shipilov, A. Zagorodnikov and F. Schroeder
Modeling Images of Proton Events for the TAIGA Project Using a Generative Adversaria Network: Features of the Network Architecture and the Learning Process
J. Dubenskaya, A. Kryukov and A. Demichev
Application of Deep Learning Technique to an Analysis of Hard Scattering Processes at Colliders
L. Dudko, P. Volkov, G. Vorotnikov and A. Zaborenko
Use of Conditional Generative Variational Autoencoder Networks to Improve Representativity of Data in Optical Spectroscopy
A. Efitorov, T. Dolenko, K. Laptinskiy, S. Burikov and S. Dolenko
A Convolutional Hierarchical Neural Network Classifier
I. Gadzhiev and S. Dolenko
The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks
E. Gres and A. Kryukov
Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks
S. Polyakov, A. Demichev, A. Kryukov and E. Postnikov
The Russian Language Corpus and a Neural Network to Analyse Internet Tweet Reports About Covid-19
A. Sboev, I. Moloshnikov, A. Naumov, A. Levochkina and R. Rybka
Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks
A.A. Vlaskina and A. Kryukov