Table of Contents

Proceedings

Jan. 18, 2024


Вышел номер Вестника МГУ с трудами конференции: https://link.springer.com/journal/11972/volumes-and-issues/78-1/supplement

The proceedings of the DLCP2023 conference will be published as a special issue of the journal Moscow University Physics Bulletin in 2023 in both electronic and paper form. The journal is published in English by Springer and indexed in the databases WoS and Scopus and is included in the Russian index RCSI too.

Paper submission deadline is August 14, 2023 → Aug. 21, 2023 → Aug. 24, 2023

After blind peer review, all accepted papers will be published in the conference proceedings.

Notification of paper acceptance — September 11, 2023 → September 25, 2023 → September 29, 2023→ October 05, 2023

The language of publications is English
All articles must additionally contain an abstract in Russian in PDF format.

We invite three types of submissions:

Important. The title of the article should be the same as in the conference program. The first author is the speaker.

Prepared articles should be sent to the program committee by e-mail: dlcp2023@spbu.ru. By submitting an article, you agree to the rules of Moscow University Physics Bulletin. In particular, you must sign the COPYRIGHT TRANSFER AGREEMENT, which must be included in the package of submitted documents.

More details can be found at journal website.

Current status

Nov.20, 2023

TeX template

Jul 29, 2023

All papers must be prepared in LaTeX by using the Template. Example is here. Before the title, the authors should indicate the name of the section in accordance with the Book of Abstracts.

At the link https://www.overleaf.com/read/hjpdhfrhbjyd, the template for articles is available on the Overleaf in read-only mode. You need to create a copy of the project in Overleaf in order to work.

Please send to dlcp2023@spbu.ru a single zip-archive (dlcp2023-<last name of speaker>-<version>.zip) containing:

The article must have a name in the same format: dlcp2023-<last name of speaker>.pdf.

Typical article formatting errors

Внимание! При повторной отсылке документов архив должен содержать полный список документов!

Более подробно правила изложены в образце.

Final version of the Proceedings

List of accepted papers

Отправляя статью в сборник трудов авторы дают согласие на ее открытую публикацию в журнале и несут полную ответственность за ее содержание.

Plenary Reports

M.I.Petrovskiy. DEEP LEARNING METHODS FOR THE TASKS OF CREATING “DIGITAL TWINS” FOR TECHNOLOGICAL PROCESSES

Track 1. Machine Learning in Fundamental Physics

Ju.Dubenskaya. Generating Synthetic Images of Gamma-Ray Events for Imaging Atmospheric Cherenkov Telescopes Using Conditional Generative Adversarial Networks
R.R.Fitagdinov. Generation of the ground detector readings of the Telescope Array experiment and the search for anomalies using neural networks
K.A.Galaktionov / Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data
E.O.Gres. The selection of gamma events from IACT images with deep learning methods
A.Kryukov. Preliminary results of convolutional neural network models in HiSCORE experiment
A.Kryukov. The use of conditional variational autoencoders for simulation of EASs images from IACTs
V.S.Latypova / Method for separating extensive air showers by primary mass using machine learning for a SPHERE-type Cherenkov telescope
A.Y.Leonov. Deep Learning for Angle of Arrival Prediction in the Baikal Neutrino Telescope
A.V. Matseiko. Application of machine learning methods in Baikal-GVD:background noise rejection and selection of neutrino induced events
A.D.Zaborenko. Novelty Detection Neural Networks for Model-Independent New Physics Search

Track 2. Machine Learning in Natural Sciences

M.Borisov. Estimating cloud base height from all-sky imagery using artificial neural networks
S.Dolenko(А.Guskov) Transfer Learning for Neural Network Solution of an Inverse Problem in Optical Spectroscopy
I.M.Gadzhiev. Classification Approach to Prediction of Geomagnetic Disturbances
V.Golikov. Client-server application for automated estimation of the material composition of bottom sediments in the >0.1 mm fraction from microphotography using modern deep learning methods
A.Kasatkin. Machine learning techniques for anomaly detection in high-frequency time series of wind speed and greenhouse gas concentration measurements
I.Khabutdinov. Identifying cetacean mammals in high-resolution optical imagery using anomaly detection approach employing Machine Learning models
M.Krinitsky. Estimating significant wave height from X-band navigation radar using convolutional neural networks
M.A.Ledovskikh. Recognition of skin lesions from image
A.V.Orekhov. Unsupervised Machine Learning Methods for Determining Special Points of the Polymerase Chain Reaction Fluorescence Accumulation Curve
S.A.Pavlov. Application of Machine Learning Methods to Numerical Simulation of Hypersonic Flow
V.Y.Rezvov. Improvement of the AI-based estimation of signifi cant wave height based on preliminary training on synthetic X-band radar sea clutter images
O.E.Sarmanova. Decoding fluorescence excitation-emission matrices of carbon dots aqueous solutions with convolutional neural networks to create multimodal nanosensor of metal ions
A.Savin. SMAP sea surface salinity improvement in the Arctic region using machine learning approaches
A.Tyshko. Automatic detection of acoustic signals from white whales and bottle-nosed dolphins
A.V. Vorobev. Machine learning for diagnostics of space weather effects in the Arctic region

Track 3. Modern Machine Learning Methods

N.Y.Bykov / Methods for a Partial Differential Equation Discovery: Application to Physical and Engineering Problems
S.Dolenko. Decomposition of Spectral Contour into Gaussian Bands using Improved Modification of Gender Genetic Algorithm
I.Isaev. The study of the integration of physical methods in the neural network solution of the inverse problem of exploration geophysics with variable physical properties of the medium
D.N.Polyakov / Hyper-parameter tuning of neural network for high-dimensional problems in the case of Helmholtz equation