Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Kalyuzhnaya, Anna"'
Autor:
Nikitin, Nikolay O., Pinchuk, Maiia, Pokrovskii, Valerii, Shevchenko, Peter, Getmanov, Andrey, Aksenkin, Yaroslav, Revin, Ilia, Stebenkov, Andrey, Poslavskaya, Ekaterina, Kalyuzhnaya, Anna V.
Automated machine learning (AutoML) systems propose an end-to-end solution to a given machine learning problem, creating either fixed or flexible pipelines. Fixed pipelines are task independent constructs: their general composition remains the same,
Externí odkaz:
http://arxiv.org/abs/2312.14770
Autor:
Starodubcev, Nikita O., Nikitin, Nikolay O., Gavaza, Konstantin G., Andronova, Elizaveta A., Sidorenko, Denis O., Kalyuzhnaya, Anna V.
In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are demonstrating intensive growth owing to promising outlook. However, existing approaches are limited b
Externí odkaz:
http://arxiv.org/abs/2207.14621
In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural
Externí odkaz:
http://arxiv.org/abs/2204.03400
Autor:
Andriushchenko, Petr, Deeva, Irina, Bubnova, Anna, Voskresenskiy, Anton, Bukhanov, Nikita, Nikitin, Nikolay, Kalyuzhnaya, Anna
The work focuses on the modelling and imputation of oil and gas reservoirs parameters, specifically, the problem of predicting the oil recovery factor (RF) using Bayesian networks (BNs). Recovery forecasting is critical for the oil and gas industry a
Externí odkaz:
http://arxiv.org/abs/2204.00413
Autor:
Nikitin, Nikolay O., Vychuzhanin, Pavel, Sarafanov, Mikhail, Polonskaia, Iana S., Revin, Ilia, Barabanova, Irina V., Maximov, Gleb, Kalyuzhnaya, Anna V., Boukhanovsky, Alexander
The effectiveness of the machine learning methods for real-world tasks depends on the proper structure of the modeling pipeline. The proposed approach is aimed to automate the design of composite machine learning pipelines, which is equivalent to com
Externí odkaz:
http://arxiv.org/abs/2106.15397
This paper describes a new library for learning Bayesian networks from data containing discrete and continuous variables (mixed data). In addition to the classical learning methods on discretized data, this library proposes its algorithm that allows
Externí odkaz:
http://arxiv.org/abs/2106.13194
Autor:
Nikitin, Nikolay O., Revin, Ilia, Hvatov, Alexander, Vychuzhanin, Pavel, Kalyuzhnaya, Anna V.
The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimization and two tasks within it: improving the quality of oil production e
Externí odkaz:
http://arxiv.org/abs/2103.02598
Autor:
Deeva, Irina, Bubnova, Anna, Andriushchenko, Petr, Voskresenskiy, Anton, Bukhanov, Nikita, Nikitin, Nikolay O., Kalyuzhnaya, Anna V.
In this paper, a multipurpose Bayesian-based method for data analysis, causal inference and prediction in the sphere of oil and gas reservoir development is considered. This allows analysing parameters of a reservoir, discovery dependencies among par
Externí odkaz:
http://arxiv.org/abs/2103.01804
Autor:
Polonskaia, Iana S., Nikitin, Nikolay O., Revin, Ilia, Vychuzhanin, Pavel, Kalyuzhnaya, Anna V.
In this paper, a multi-objective approach for the design of composite data-driven mathematical models is proposed. It allows automating the identification of graph-based heterogeneous pipelines that consist of different blocks: machine learning model
Externí odkaz:
http://arxiv.org/abs/2103.01301
In the paper, we propose an adaptive data-driven model-based approach for filling the gaps in time series. The approach is based on the automated evolutionary identification of the optimal structure for a composite data-driven model. It allows adapti
Externí odkaz:
http://arxiv.org/abs/2103.01124