80 citations · 183 across the 13 of their papers we have counts for
15 papers
MADD: Multi-Agent Drug Discovery Orchestra
Gleb V. Solovev, Alina B. Zhidkovskaya, Anastasia Orlova +18
Hit identification is a central challenge in early drug discovery, traditionally requiring substantial experimental resources. Recent advances in artificial intelligence, particula…
Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines
Nikolay O. Nikitin, Maiia Pinchuk, Valerii Pokrovskii +7
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 tas…
Generative Design of Physical Objects using Modular Framework
Nikita O. Starodubcev, Nikolay O. Nikitin, Konstantin G. Gavaza +3
In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are demonstrating intensive growth…
Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks
Nikita O. Starodubcev, Nikolay O. Nikitin, Anna V. Kalyuzhnaya
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 computa…
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…
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…
MIxBN: library for learning Bayesian networks from mixed data
Anna V. Bubnova, Irina Deeva, Anna V. Kalyuzhnaya
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…
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…
Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea
Nikolay O. Nikitin, Ilia Revin, Alexander Hvatov +2
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 optimizat…
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…
REBEC: Robust Evolutionary-based Calibration Approach for the Numerical Wind Wave Model
Pavel Vychuzhanin, Nikolay O. Nikitin, Anna V. Kalyuzhnaya
The adaptation of numerical wind wave models to the local time-spatial conditions is a problem that can be solved by using various calibration techniques. However, the obtained set…
Data-driven PDE discovery with evolutionary approach
Michail Maslyaev, Alexander Hvatov, Anna Kalyuzhnaya
The data-driven models allow one to define the model structure in cases when a priori information is not sufficient to build other types of models. The possible way to obtain physi…
Adaptation of NEMO-LIM3 model for multigrid high resolution Arctic simulation
Alexander Hvatov, Nikolay O. Nikitin, Anna V. Kalyuzhnaya +1
High-resolution regional hindcasting of ocean and sea ice plays an important role in the assessment of shipping and operational risks in the Arctic Ocean. The ice-ocean model NEMO-…
A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification
Sergey V. Kovalchuk, Oleg G. Metsker, Anastasia A. Funkner +5
Complex systems' modeling and simulation are powerful ways to investigate a multitude of natural phenomena providing extended knowledge on their structure and behavior. However, en…