Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Hvatov, Alexander"'
This paper explores the critical role of differentiation approaches for data-driven differential equation discovery. Accurate derivatives of the input data are essential for reliable algorithmic operation, particularly in real-world scenarios where m
Externí odkaz:
http://arxiv.org/abs/2311.05787
Autor:
Hvatov, Alexander, Titov, Roman
Differential equation discovery, a machine learning subfield, is used to develop interpretable models, particularly in nature-related applications. By expertly incorporating the general parametric form of the equation of motion and appropriate differ
Externí odkaz:
http://arxiv.org/abs/2308.04901
Autor:
Ivanchik, Elizaveta, Hvatov, Alexander
The discovery of equations with knowledge of the process origin is a tempting prospect. However, most equation discovery tools rely on gradient methods, which offer limited control over parameters. An alternative approach is the evolutionary equation
Externí odkaz:
http://arxiv.org/abs/2308.04996
Autor:
Maslyaev, Mikhail, Hvatov, Alexander
Evolutionary differential equation discovery proved to be a tool to obtain equations with less a priori assumptions than conventional approaches, such as sparse symbolic regression over the complete possible terms library. The equation discovery fiel
Externí odkaz:
http://arxiv.org/abs/2306.17038
Autor:
Golovanev, Yakov, Hvatov, Alexander
Most machine learning methods are used as a black box for modelling. We may try to extract some knowledge from physics-based training methods, such as neural ODE (ordinary differential equation). Neural ODE has advantages like a possibly higher class
Externí odkaz:
http://arxiv.org/abs/2206.03304
Autor:
Hvatov, Alexander, Tikhonova, Tatiana
The numerical methods for differential equation solution allow obtaining a discrete field that converges towards the solution if the method is applied to the correct problem. Nevertheless, the numerical methods have the restricted class of the equati
Externí odkaz:
http://arxiv.org/abs/2205.05383
Autor:
Hvatov, Alexander, Maslyaev, Mikhail, Polonskaya, Iana S., Sarafanov, Mikhail, Merezhnikov, Mark, Nikitin, Nikolay O.
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 properties of the model, which parts could be replaced to obtain better result
Externí odkaz:
http://arxiv.org/abs/2107.03146
Autor:
Maslyaev, Mikhail, Hvatov, Alexander
Usually, the systems of partial differential equations (PDEs) are discovered from observational data in the single vector equation form. However, this approach restricts the application to the real cases, where, for example, the form of the external
Externí odkaz:
http://arxiv.org/abs/2103.06739
Autor:
Hvatov, Alexander
The numerical solution methods for partial differential equation (PDE) solution allow obtaining a discrete field that converges towards the solution if the method is applied to the correct problem. Nevertheless, the numerical methods usually have the
Externí odkaz:
http://arxiv.org/abs/2103.02294
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