80 citations · 82 across the 4 of their papers we have counts for
8 papers
Oil reservoir recovery factor assessment using Bayesian networks based on advanced approaches to analogues clustering
Petr Andriushchenko, Irina Deeva, Anna Bubnova +4
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…
Model-agnostic multi-objective approach for the evolutionary discovery of mathematical models
Alexander Hvatov, Mikhail Maslyaev, Iana S. Polonskaya +3
In modern data science, it is often not enough to obtain only a data-driven model with a good prediction quality. On the contrary, it is more interesting to understand the properti…
Automated Evolutionary Approach for the Design of Composite Machine Learning Pipelines
Nikolay O. Nikitin, Pavel Vychuzhanin, Mikhail Sarafanov +6
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 desi…
Oil and Gas Reservoirs Parameters Analysis Using Mixed Learning of Bayesian Networks
Irina Deeva, Anna Bubnova, Petr Andriushchenko +4
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…
Multi-Objective Evolutionary Design of Composite Data-Driven Models
Iana S. Polonskaia, Nikolay O. Nikitin, Ilia Revin +2
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 heterogene…
Automated data-driven approach for gap filling in the time series using evolutionary learning
Mikhail Sarafanov, Nikolay O. Nikitin, Anna V. Kalyuzhnaya
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…