Haungs, Andreas

Kryukov, Alexander

English

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PoS(DLCP2021)001The 5th International Workshop on Deep Learning inComputational Physics – DLCP-2021Andreas Haungs𝑎,∗ and Alexander Kryukov𝑏𝑎Karlsruhe Institute of Technology, Institute for Astroparticle Physics, Karlsruhe, Germany𝑏Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics, Moscow, RussiaE-mail: andreas.haungs@kit.edu, kryukov@theory.sinp.msu.ruPreface to the 5𝑡ℎ International Workshop on Deep Learning in Computational Physics(DLCP-2021) hold online on 28-29 June, 2021.The 5th International Workshop on Deep Learning in Computational Physics28-29 June, 2021Moscow, Russia∗Speaker© Copyright owned by the author(s) under the terms of the Creative CommonsAttribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). https://pos.sissa.it/PoS(DLCP2021)001DLCP-2021 Andreas HaungsThe international workshop DLCP-2021, hold online on June 28th and 29th, 2021, was dedi-cated on "Deep Learning in Computational Physics".The workshop primarily focuses on the use of machine learning in cosmic-ray astrophysics, butwas not limited to this area. Topics of interest are various applications of artificial neural networksto physical problems, as well as the development of new modern machine learning methods foranalysing various scientific data, including big data.Modern large-scale physics experiments generate a huge data stream, measured in at leasthundreds of terabytes. The time of their active operation can reach several decades, and the amountof accumulated data can exceed one hundred petabytes. In this context, the issue of active andon-going management of data as well as modern and sophisticated analyses methods throughouttheir life cycle is a very important and highly topical issue.Figure 1: General data life cycle scheme in physics, where the DLCP-2021 was dedicated to the developmentof modern analysis methods. For a useful and efficient life cycle each step must be based on a FAIR data andmetadata treatment.This workshop is part of a cooperation between Russia and Germany, funded by Helmholtzand RSF, dedicated to the digitisation of astroparticle physics [1, 2]. The baseline approach of thiscooperation is to establish FAIR Data Life Cycle Concepts and Open Data in the research field. Atypical Data Life Cycle is shown in Fig. 1. DLCP-2021 was dedicated to two stages of data lifecycle: (i) processing data and (ii) analysing data. These are key items for the success of any modernexperiment.Currently, methods of data processing and data analysis based on machine learning are activelydeveloping in various fields of science and technology. These methods have shown outstandingresults in the field of natural language text analysis, face recognition, social network analysis, and so2PoS(DLCP2021)001DLCP-2021 Andreas Haungson. These methods have become more and more applied in natural sciences as physics, chemistry,medicine, in problems of climate modelling or in engineering sciences. Therefore, the DLCP-2021workshop was devoted to machine learning methods for processing and analysing data from scien-tific experiments. In particular, special attention has been paid to problems in astroparticle physics.The current workshop was attended by 32 scientists from Russia and Germany as well asfrom other countries. In total, the scientific editor board selected 17 articles for publication, whichpresent the results of original research.References[1] Proceedings of the 3rd International Workshop on Data Life Cycle in Physics (DLCP-2019),Irkutsk, Russia , April 2-7, 2019. Edited by: Alexander Kryukov, Andreas Haungs; CEUR-WS: 12 Jul 2019, http://ceur-ws.org/Vol-2406/[2] Proceedings of the 4th International Workshop on Data Life Cycle in Physics (DLCP-2020),Moscow, Russia, June 8-10, 2020. Edited by: Alexander Kryukov, Andreas Haungs; CEUR-WS: 21 Sep 2020, http://ceur-ws.org/Vol-2679/3

The 5th International Workshop on Deep Learning in Computational Physics (DLCP2021) [Editorial]

https://publikationen.bibliothek.kit.edu/1000141280/136497275

The 5th International Workshop on Deep Learning in Computational Physics (DLCP2021) [Editorial]

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