arXiv subjects

E. Illarionov

Publications and source records attributed to E. Illarionov.

7 recordsLinked to original sources

arXiv2026

Neural Differential Equations for the Solar Dynamo

Physical models aimed to reproduce basic features of the solar sunspot cycle are typically based on the solar dynamo mechanism. Usually qualitative arguments are used to define parameters of the model, among which a challenging component is the nonlinear form of quenching of the alpha-effect governing regeneration of the magnetic field. We propose a novel approach, in which the functional form of the alpha-quenching is represented by a neural network model embedded into neural differential dynamo equations trained on observational data. For demonstration, we consider a low-mode dynamo model and find a wide set of alpha-quenching functions and corresponding dynamo numbers that provide an accurate fit to the average profile of the solar cycle data given by sunspot numbers. Within this set, we observe a strong relationship between the dynamo number and the shape of the alpha-quenching function indicating that additional magnetic field data or constraints are essential to unambiguously infer parameters of the dynamo model. In our opinion, the neural differential approach opens a new prospect for data-driven investigation of the closure problem in dynamo theory.

astro-ph.SR↗

arXiv2025

Memory time fluctuations and instabilities in random media

The role of memory time fluctuations for instabilities in random media is considered. It is shown that fluctuations can result in infinitely fast growth of statistical moments. The effect is demonstrated in the framework of light propagation in the Universe, which contains curvature fluctuations while remaining homogeneous and isotropic on average.

physics.plasm-ph↗

arXiv2024

Machine learning for reconstruction of polarity inversion lines from solar filaments

Solar filaments are well-known tracers of polarity inversion lines that separate two opposite magnetic polarities on the solar photosphere. Because observations of filaments began long before the systematic observations of solar magnetic fields, historical filament catalogs can facilitate the reconstruction of magnetic polarity maps at times when direct magnetic observations were not yet available. In practice, this reconstruction is often ambiguous and typically performed manually. We propose an automatic approach based on a machine-learning model that generates a variety of magnetic polarity maps consistent with filament observations. To evaluate the model and discuss the results we use the catalog of solar filaments and polarity maps compiled by McIntosh. We realize that the process of manual compilation of polarity maps includes not only information on filaments, but also a large amount of prior information, which is difficult to formalize. In order to compensate for the lack of prior knowledge for the machine-learning model, we provide it with polarity information at several reference points. We demonstrate that this process, which can be considered as the user-guided reconstruction or super-resolution, leads to polarity maps that are reasonably close to hand-drawn ones, and additionally allows for uncertainty estimation.

cs.LG↗

arXiv2021

End-to-end neural network approach to 3D reservoir simulation and adaptation

Reservoir simulation and adaptation (also known as history matching) are typically considered as separate problems. While a set of models are aimed at the solution of the forward simulation problem assuming all initial geological parameters are known, the other set of models adjust geological parameters under the fixed forward simulation model to fit production data. This results in many difficulties for both reservoir engineers and developers of new efficient computation schemes. We present a unified approach to reservoir simulation and adaptation problems. A single neural network model allows a forward pass from initial geological parameters of the 3D reservoir model through dynamic state variables to well's production rates and backward gradient propagation to any model inputs and variables. The model fitting and geological parameters adaptation both become the optimization problem over specific parts of the same neural network model. Standard gradient-based optimization schemes can be used to find the optimal solution. Using real-world oilfield model and historical production rates we demonstrate that the suggested approach allows reservoir simulation and history matching with a benefit of several orders of magnitude simulation speed-up. Finally, to propagate this research we open-source a Python-based framework DeepField that allows standard processing of reservoir models and reproducing the approach presented in this paper.

cs.LG↗

arXiv2018

Segmentation of coronal holes in solar disk images with a convolutional neural network

Current coronal holes segmentation methods typically rely on image thresholding and require non-trivial image pre- and post-processing. We have trained a neural network that accurately isolates CHs from SDO/AIA 193 Angstrom solar disk images without additional complicated steps. We compare results with publicly available catalogues of CHs and demonstrate stability of the neural network approach. In our opinion, this approach can outperform hand-engineered solar image analysis and will have a wide application to solar data. In particular, we investigate long-term variations of CH indices within the solar cycle 24 and observe increasing of CH areas in about three times from minimal values in the maximum of the solar cycle to maximal values during the declining phase of the solar cycle.

astro-ph.SR↗

arXiv2014

The Properties of the Tilts of Bipolar Solar Regions

We investigate various properties associated with the tilt of isolated magnetic bipoles in magnetograms taken at the solar surface. We show that bipoles can be divided into two groups which have tilts of opposite signs, and reveal similar properties with respect to bipole area, flux and bipolar moment. Detailed comparison of these physical quantities shows that the dividing point between the two types of bipoles corresponds to a bipole area of about 300 millionths of the solar hemisphere (MHS). The time-latitude distribution of small bipoles differs substantially from that for large bipoles. Such behaviour in terms of dynamo theory may indicate that small and large bipoles trace different components of the solar magnetic field. The other possible viewpoint is that the difference in tilt data for small and large bipoles is connected with spectral helicity separation, which results in opposite tilts for small and large bipoles. We note that the data available do not provide convincing reasons to prefer either interpretation.

astro-ph.SR↗

arXiv2013

A new dynamo pattern revealed by the tilt angle of bipolar sunspot groups

We obtain the latitude-time distribution of the averaged tilt angle of solar bipoles. For large bipoles, which are mainly bipolar sunspot groups, the spatially averaged tilt angle is positive in the Northern solar hemisphere and negative in the Southern, with modest variations during course of the solar cycle. We consider the averaged tilt angle to be a tracer for a crucial element of the solar dynamo, i.e. the regeneration rate of poloidal large-scale magnetic field from toroidal. The value of the tilt obtained crudely corresponds to a regeneration factor corresponding to about 10% of r.m.s. velocity of solar convection. These results develop findings of Kosovichev and Stenflo (2012) concerning Joy's law, and agree with the usual expectations of solar dynamo theory. Quite surprisingly, we find a pronounced deviation from these properties for smaller bipoles, which are mainly solar ephemeral regions. They possess tilt angles of approximately the same absolute value, but of opposite sign compared to that of the large bipoles. Of course, the tilt data for small bipoles are less well determined than those for large bipoles; however they remain robust under various modifications of the data processing.

astro-ph.SR↗