Table of Contents
Download Call for Paper
Please note that during the white nights in St. Petersburg there is a large influx of tourists. Therefore, it is highly recommended to book hotels in advance.
The 7th International Conference on Deep Learning in Computational Physics
June 21-23, 2023
SPbSU, St.-Petersburg, Peterhof, Russia
Official website DLCP2023: https://dlcp2023.sinp.msu.ru
We are pleased to invite you to participate to the DLCP-2023 – The 7th International Conference on Deep Learning in Computational Physics which will be held at the Faculty of Physics of St.-Petersburg State University (SPbSU) on June 21-23, 2023.
The conference will be held in a mixed format: face-to-face and remote. However, the poster section will be held in face-to-face format only.
The conference 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 working languages are English and Russian.
St.-Petersburg State University (SPbSU, Saint-Petersburg, Russia),
M.V. Lomonosov Moscow State University, D.V. Skobeltsyn Institute of Nuclear Physics (SINP MSU, Moscow, Russia),
Research Computing Center of the Lomonosov Moscow State University (RCC MSU, Moscow, Russia)
Joint Institute for Nuclear Research, Meshcheryakov Laboratory of Information Technologies (MLIT JINR, Dubna, Russia)
The main topics
Track 1. Machine Learning in Physics
- Machine learning methods in particle astrophysics and high energy physics.
- Fast event generators based on machine learning for simulation of physics phenomena.
- Multi-messenger data analysis of experimental data.
- Application machine learning for data analysis in megascience 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
The conference will feature:
- invited presentations – 30 minutes,
- regular presentations – 15 minutes,
- posters (only face to face) – A1 or A0 format, vertical arrangement.
- Registration — until June 7, 2023
- Deadline for abstract submission —
May 23, 2023June 1, 2023
- Notification of report acceptance — June 7, 2023
- Conference dates - June 21-23, 2023
- Paper submission — August 14, 2023
A fee is charged for participation in the conference. Registration fee includes organizing costs, participation support, coffee/tea breaks, and conference proceedings. Accommodation and social events are not included, participants pay for it on their own.
On site participation:
- Participant - 6000 ₽
- Student - 1000 ₽
Online (remote participation):
- Рarticipants with accepted report - 3000 ₽
- Student, online participant without report - free of charge
Registration and abstract submission
The registration and abstract submission should be done via the website: Registration and abstract submission.
After blind peer review, all accepted papers will be published online in the conference proceedings at Proceedings of Science and submitted for indexing in Scopus and supply by DOI.
We invite two types of submissions:
REGULAR PAPERS describe research not published or submitted elsewhere (10-12 pages). SHORT PAPERS may be position papers, descriptions of research prospects, challenges, projects, ongoing works, or applications (5-9 pages).
Place and transportation
The Faculty of Physics of SPbSU (Uljanovskaja str., 3, St-Petersburg-Peterhof, Russia). Directions to the meeting venue, information about trains and buses can be found on the conference website. The ZOOM link will only be sent to registered participants 2 days before the start of the conference.
Conference participants have to make hotel reservations on their own and in advance. Some hotels in Peterhof:
- Hotel Samson- https://samson-hotel.ru/
- Belveder Hotel &Spa - http://belveder-hotel.ru/
- MiniHotel Marli - http://marlihotel.ru/
- Hotel Aleksandria-Petergof - https://aleksandria-petergof.ru/
For participants, it is possible to stay in the hostel of the Campus, which is located next to the conference venue. Double-room (combined in a block with a triple-room, shared shower) , the cost is 650 ₽ per day for 1 bed. When filling out the registration form, please be sure to indicate the information about the need to book a place in the hostel!
- A. Kryukov (SINP MSU, Moscow) — Co-Chair
- S. Yakovlev, professor (SPbSU,SPb) — Co-Chair
- I. Andronov, professor (MMAA, SPb)
- I. Belyaev, professor (Herzen University, SPb)
- E. Boos, corresponding member of the RAS (SINP MSU, Moscow)
- A. Demichev (SINP MSU, Moscow)
- V. Ilyin (NIC “Kurchatov Institute”, Moscow)
- V. Korenkov, professor (MLIT JINR, Dubna)
- V. Rudnev, (SPbSU, SPb)
- A. Tzherbakov, professor (IAI RAS, SPb)
- М. Stepanova, (SPbSU, SPb)
- F. Valiev, professor (SPbSU,SPb)
- V. Voevodin, corresponding member of the RAS (RCC MSU, Moscow)
- E. Yarevsky, professor (SPbSU,SPb)
- М. Stepanova, (SPbSU, SPb) - Executive Secretary
- A. Demichev (SINP MSU, Moscow)
- A. Kryukov (SINP MSU, Moscow)
- V. Rudnev, (SPbSU, SPb)
- T. Trushina, (SPbSU, SPb)
- M. Andreev, (SPbSU, SPb)
- D. Polyakov, (SPbSU, SPb)
All correspondence should be addressed by e-mail: firstname.lastname@example.org.
Группа компаний РСК — ведущий российский и хорошо известный в мире разработчик и интегратор «полного цикла» инновационных энергоэффективных, высокоплотных, масштабируемых решений с жидкостным охлаждением для высокопроизводительных вычислений и систем для машинного/глубокого обучения (High-Performance Computing, Machine Learning/Deep Learning), центров обработки данных (ЦОД), Edge Computing решений и интеллектуальных систем хранения данных «по требованию» (storage-on-demand).