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Mathematics
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Intelligence Computing and Optimization Methods in Natural Sciences
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Intelligence Computing and Optimization Methods in Natural Sciences

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 1279

Special Issue Editor

Prof. Dr. Peter S. Martyshko

E-Mail Website
Guest Editor
Bulashevich Institute of Geophysics, Ural Branch of Russian Academy of Science, 620016 Yekaterinburg, Russia
Interests: inverse geophysical problems; numerical methods & mathematical modeling; inversion theory; exploration geophysics; unstable problems

Special Issue Information

Dear Colleagues,

I invite you to submit your latest research in the area of Intelligence Computing  and  Mathematical Optimization Methods to this Special Issue “Intelligence Computing and Optimization Methods in Natural Sciences”.

This Issue deals with aspects of mathematical modeling and the development of innovative novel algorithms for the solution of various types of optimization problems in natural sciences.

Optimization problems arise in all fields in the real world and have immense importance.

Mathematical optimization methods and intelligence computing are two advanced technologies that are used in a various fields of applications. Both are applied mathematics methods to solve complex problems. Both technologies have a wide range of applications.

These techniques are necessary for creating mathematical models of physical processes and phenomena, geophysical models of geological structures, etc. Optimization method operates with detailed mathematical models and Intelligence Computing methods can be used for estimating the parameters of models from the standpoint of probability theory (including variance or noise in the specific data values).

High-quality papers that address both theoretical and practical issues in the area of optimization and Intelligence Computing, and submissions that present new theoretical results, models and algorithms, as well as new applications, real world case study are welcome.

Submitted papers should satisfy the general requirements of the Mathematics journal, with a strong focus on new analytic or numerical methods for solving challenging problems.

Potential topics include, but are not limited to, applications of numerical and continuous optimization methods, mathematical models, optimization techniques, and machine learning algorithms in natural sciences; high-performance computing for mathematical modeling, stochastic optimization, fuzzy logic, artificial neural networks and evolutionary modeling.

Prof. Dr. Peter S. Martyshko
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical optimization methods
  • artificial neural networks
  • inverse problems
  • mathematical modeling
  • fuzzy logic
  • stochastic optimization

Published Papers (2 papers)

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Research

16 pages, 8958 KiB  
Article
An Algorithm for Solving the Problem of Phase Unwrapping in Remote Sensing Radars and Its Implementation on Multicore Processors
by Petr S. Martyshko, Elena N. Akimova, Andrey V. Sosnovsky and Victor G. Kobernichenko
Mathematics 2024, 12(5), 727; https://doi.org/10.3390/math12050727 - 29 Feb 2024
Viewed by 455
Abstract
The problem of the interferometric phase unwrapping in radar remote sensing of Earth systems is considered. Such interferograms are widely used in the problems of creating and updating maps of the relief of the Earth’s surface in geodesy, cartography, environmental monitoring, geological, hydrological [...] Read more.
The problem of the interferometric phase unwrapping in radar remote sensing of Earth systems is considered. Such interferograms are widely used in the problems of creating and updating maps of the relief of the Earth’s surface in geodesy, cartography, environmental monitoring, geological, hydrological and glaciological studies, and for monitoring transport communications. Modern radar systems have ultra-high spatial resolution and a wide band, which leads to the need to unwrap large interferograms from several tens of millions of elements. The implementation of calculations by these methods requires a processing time of several days. In this paper, an effective method for equalizing the inverse vortex field for phase unwrapping is proposed, which allows solving a problem with quasi-linear computational complexity depending on the interferogram size and the number of singular points on it. To implement the method, a parallel algorithm for solving the problem on a multi-core processor using OpenMP technology was developed. Numerical experiments on radar data models were carried out to investigate the effectiveness of the algorithm depending on the size of the source data, the density of singular points and the number of processor cores. Full article
(This article belongs to the Special Issue Intelligence Computing and Optimization Methods in Natural Sciences)
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24 pages, 6959 KiB  
Article
Transient Electromagnetic Monitoring of Permafrost: Mathematical Modeling Based on Sumudu Integral Transform and Artificial Neural Networks
by Viacheslav Glinskikh, Oleg Nechaev, Igor Mikhaylov, Marina Nikitenko and Kirill Danilovskiy
Mathematics 2024, 12(4), 585; https://doi.org/10.3390/math12040585 - 16 Feb 2024
Viewed by 521
Abstract
Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude [...] Read more.
Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude regions. In order to prevent the negative consequences of permafrost thawing under the foundations of constructions, various geophysical techniques for monitoring permafrost have been proposed and applied so far: temperature, electrical, seismic and many others. We propose a cross-borehole exploration system for a high localization of target objects in the cryolithozone. A novel mathematical apparatus for three-dimensional modeling of transient electromagnetic signals by the vector finite element method has been developed. The original combination of the latter, the Sumudu integral transform and artificial neural networks makes it possible to examine spatially heterogeneous objects of the cryolithozone with a high contrast of geoelectric parameters, significantly reducing computational costs. We consider numerical simulation results of the transient electromagnetic monitoring of industrial facilities located on permafrost. The formation of a talik has been shown to significantly manifest itself in the measured electromagnetic responses, which enables timely prevention of industrial disasters and environmental catastrophes. Full article
(This article belongs to the Special Issue Intelligence Computing and Optimization Methods in Natural Sciences)
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