Výsledky vyhledávání - "Toktaliev, P."

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Greedy Kernel Methods for Approximating Breakthrough Curves for Reactive Flow from 3D Porous Geometry Data

Autor: Herkert, Robin, Buchfink, Patrick, Wenzel, Tizian, Haasdonk, Bernard, Toktaliev, Pavel, Iliev, Oleg

We address the challenging application of 3D pore scale reactive flow under varying geometry parameters. The task is to predict time-dependent integral quantities, i.e., breakthrough curves, from the given geometries. As the 3D reactive flow simulati

Externí odkaz: http://arxiv.org/abs/2405.19170

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Machine learning methods for prediction of breakthrough curves in reactive porous media

Autor: Fokina, Daria, Toktaliev, Pavel, Iliev, Oleg, Oseledets, Ivan

Reactive flows in porous media play an important role in our life and are crucial for many industrial, environmental and biomedical applications. Very often the concentration of the species at the inlet is known, and the so-called breakthrough curves

Externí odkaz: http://arxiv.org/abs/2301.04998

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On the Performance of Machine Learning Methods for Breakthrough Curve Prediction

Autor: Fokina, Daria, Iliev, Oleg, Toktaliev, Pavel, Oseledets, Ivan, Schindler, Felix

Reactive flows are important part of numerous technical and environmental processes. Often monitoring the flow and species concentrations within the domain is not possible or is expensive, in contrast, outlet concentration is straightforward to measu

Externí odkaz: http://arxiv.org/abs/2204.11719

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On a workflow for efficient computation of the permeability of tight sandstones

Autor: Pimanov, Vladislav, Lukoshkin, Vladislav, Toktaliev, Pavel, Iliev, Oleg, Muravleva, Ekaterina, Orlov, Denis, Krutko, Vladislav, Avdonin, Alexander, Steiner, Konrad, Koroteev, Dmitry

The paper presents a workflow for fast pore-scale simulation of single-phase flow in tight reservoirs typically characterized by low, multiscale porosity. Multiscale porosity implies that the computational domain contains porous voxels (unresolved po

Externí odkaz: http://arxiv.org/abs/2203.11782

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A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows

Autor: Gavrilenko, Pavel, Haasdonk, Bernard, Iliev, Oleg, Ohlberger, Mario, Schindler, Felix, Toktaliev, Pavel, Wenzel, Tizian, Youssef, Maha

We present an integrated approach for the use of simulated data from full order discretization as well as projection-based Reduced Basis reduced order models for the training of machine learning approaches, in particular Kernel Methods, in order to a

Externí odkaz: http://arxiv.org/abs/2104.02800

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