Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Muravleva, Ekaterina"'
We present ConDiff, a novel dataset for scientific machine learning. ConDiff focuses on the diffusion equation with varying coefficients, a fundamental problem in many applications of parametric partial differential equations (PDEs). The main novelty
Externí odkaz:
http://arxiv.org/abs/2406.04709
Large linear systems are ubiquitous in modern computational science. The main recipe for solving them is iterative solvers with well-designed preconditioners. Deep learning models may be used to precondition residuals during iteration of such linear
Externí odkaz:
http://arxiv.org/abs/2405.15557
Autor:
Merkulov, Daniil, Cherniuk, Daria, Rudikov, Alexander, Oseledets, Ivan, Muravleva, Ekaterina, Mikhalev, Aleksandr, Kashin, Boris
In this paper, we introduce an algorithm for data quantization based on the principles of Kashin representation. This approach hinges on decomposing any given vector, matrix, or tensor into two factors. The first factor maintains a small infinity nor
Externí odkaz:
http://arxiv.org/abs/2404.09737
Autor:
Rudikov, Alexander, Fanaskov, Vladimir, Muravleva, Ekaterina, Laevsky, Yuri M., Oseledets, Ivan
Deep learning solvers for partial differential equations typically have limited accuracy. We propose to overcome this problem by using them as preconditioners. More specifically, we apply discretization-invariant neural operators to learn preconditio
Externí odkaz:
http://arxiv.org/abs/2402.05598
In this paper, we investigate the structure of the Schur complement matrix for the fully-staggered finite-difference discretization of the stationary Stokes equation. Specifically, we demonstrate that the structure of the Schur complement matrix depe
Externí odkaz:
http://arxiv.org/abs/2309.01255
If the Stokes equations are properly discretized, it is known that the Schur complement matrix is spectrally equivalent to the identity matrix. Moreover, in the case of simple geometries, it is often observed that most of its eigenvalues are equal to
Externí odkaz:
http://arxiv.org/abs/2307.05266
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
Publikováno v:
Phys. Rev. E 101, 043308 (2020)
Work considers the usage of StyleGAN architecture for the task of microstructure synthesis. The task is the following: given number of samples of structure we try to generate similar samples at the same time preserving its properties. Since the consi
Externí odkaz:
http://arxiv.org/abs/1909.07042
Autor:
Volkhonskiy, Denis, Muravleva, Ekaterina, Sudakov, Oleg, Orlov, Denis, Belozerov, Boris, Burnaev, Evgeny, Koroteev, Dmitry
In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes act
Externí odkaz:
http://arxiv.org/abs/1901.10233
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