The 5th International Workshop on Deep Learning in Computational Physics

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

Sessions
Editorial
Regular papers
Short papers

Editorial
The 5th International Workshop on Deep Learning in Computational Physics – DLCP-2021 PoS(DLCP2021)001 pdf A. Haungs and A. Kryukov
Regular papers
Equivariant Gaussian Processes as Limiting Convolutional Networks with Infinite Number of Channels PoS(DLCP2021)002 pdf A. Demichev
Neural Network Solution of Inverse Problems of Geological Prospecting with Discrete Output PoS(DLCP2021)003 pdf I. Isaev, I. Obornev, E. Obornev, E. Rodionov, M. Shimelevich and S. Dolenko
Graph Neural Networks and Application for Cosmic-Ray Analysis PoS(DLCP2021)004 pdf P. Koundal
Artificial Neural Networks for the Identification of Partial Differential Equations of Land Surface Schemes in Climate Models PoS(DLCP2021)005 pdf 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 PoS(DLCP2021)006 pdf A. Sboev, A. Selivanov, R. Rybka, I. Moloshnikov and G. Rylkov
Using Modern Machine Learning Methods on KASCADE Data for Outreach and Education PoS(DLCP2021)007 pdf V. Tokareva, D. Kostunin, I. Plokhikh and V. Sotnikov
Gamma/Hadron Separation for a Ground Based IACT in Experiment TAIGA Using Machine Learning Methods PoS(DLCP2021)008 pdf 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 PoS(DLCP2021)010 pdf 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 PoS(DLCP2021)011 pdf J. Dubenskaya, A. Kryukov and A. Demichev
Application of Deep Learning Technique to an Analysis of Hard Scattering Processes at Colliders PoS(DLCP2021)012 pdf L. Dudko, P. Volkov, G. Vorotnikov and A. Zaborenko
Use of Conditional Generative Variational Autoencoder Networks to Improve Representativity of Data in Optical Spectroscopy PoS(DLCP2021)013 pdf A. Efitorov, T. Dolenko, K. Laptinskiy, S. Burikov and S. Dolenko
A Convolutional Hierarchical Neural Network Classifier PoS(DLCP2021)014 pdf I. Gadzhiev and S. Dolenko
The Preliminary Results on Analysis of TAIGA-IACT Images Using Convolutional Neural Networks PoS(DLCP2021)015 pdf E. Gres and A. Kryukov
Processing Images from Multiple IACTs in the TAIGA Experiment with Convolutional Neural Networks PoS(DLCP2021)016 pdf 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 PoS(DLCP2021)017 pdf A. Sboev, I. Moloshnikov, A. Naumov, A. Levochkina and R. Rybka
Analysis of the HiSCORE Simulated Events in TAIGA Experiment Using Convolutional Neural Networks PoS(DLCP2021)018 pdf A.A. Vlaskina and A. Kryukov