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SCImago
Q2
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Q2
Impact factor
3
SJR
0.674
CiteScore
6.1
Categories
Chemistry (miscellaneous)
Condensed Matter Physics
Materials Science (miscellaneous)
Areas
Chemistry
Materials Science
Physics and Astronomy
Years of issue
1979-2025
journal names
Solid State Ionics
Top-3 citing journals

Journal of Power Sources
(27322 citations)

Solid State Ionics
(22036 citations)

Journal of the Electrochemical Society
(16899 citations)
Top-3 organizations

Tohoku University
(263 publications)

National Institute of Advanced Industrial Science and Technology
(181 publications)

Max Planck Institute for Solid State Research
(159 publications)

Ural Federal University
(20 publications)

Kyoto University
(18 publications)

National Institute of Advanced Industrial Science and Technology
(17 publications)
Most cited in 5 years
Found
Publications found: 110

Application of Neural Networks for Solving Nonlinear Boundary Problems for Complex-Shaped Domains
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2025
,
citations by CoLab: 0
,
Galaburdin A.V.

Open Access
|
Abstract
Introduction. Many practically significant tasks reduce to nonlinear differential equations. In this study, one of the applications of neural networks for solving specific nonlinear boundary problems for complex-shaped domains is considered. Specifically, the focus is on solving a stationary heat conduction differential equation with a thermal conductivity coefficient dependent on temperature.Materials and Methods. The original nonlinear boundary problem is linearized through Kirchhoff transformation. A neural network is constructed to solve the resulting linear boundary problem. In this context, derivatives of singular solutions to the Laplace equation are used as activation functions, and these singular points are distributed along closed curves encompassing the boundary of the domain. The weights of the network were tuned by minimizing the mean squared error of training. Results. Results for the heat conduction problem are obtained for various complex-shaped domains and different forms of dependence of the thermal conductivity coefficient on temperature. The results are presented in tables that contain the exact solution and the solution obtained using the neural network. Discussion and Conclusion. Based on the computational results, it can be concluded that the proposed method is sufficiently effective for solving the specified type of boundary problems. The use of derivatives of singular solutions to the Laplace equation as activation functions appears to be a promising approach.

Identification of Marine Oil Spills Using Neural Network Technologies
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2025
,
citations by CoLab: 0
,
Sidoryakina V.V., Solomakha D.A.

Open Access
|
Abstract
Introduction. Detecting oil spills is a critical task in monitoring the marine ecosystem, protecting it, and minimizing the consequences of emergency situations. The development of fast and accurate methods for detecting and mapping oil spills at sea is essential for prompt assessment and response to emergencies. High-resolution aerial photography provides researchers with a tool for remote monitoring of water discoloration. Artificial intelligence technologies contribute to improving and automating the interpretation and analysis of such images. This study aims to develop approaches for identifying oil spilled on water surfaces using neural networks and machine learning techniques.Materials and Methods. Algorithms capable of automatically identifying marine oil spills were developed using computer image analysis and machine learning methods. The U-Net convolutional neural network was employed for image segmentation tasks. The neural network architecture was designed using the PyTorch library implemented in Python. The AdamW optimizer was chosen for training the network. The neural network was trained on a dataset comprising 8,700 images.Results. The performance of oil spill detection on water surfaces was evaluated using metrics such as IoU, Precision, Recall, Accuracy, and F1 score. Calculations based on these metrics demonstrated identification accuracy of approximately 83–88%, confirming the efficiency of the algorithms used.Discussion and Conclusion. The U-Net convolutional network was successfully trained and demonstrated high accuracy in detecting marine oil spills on the given dataset. Future work will focus on developing algorithms using more advanced neural network models and image augmentation methods.

Forecasting the Dynamics of Summer Phytoplankton Species based on Satellite Data Assimilation Methods
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2025
,
citations by CoLab: 0
,
Belova Y.V., Filina A.A., Chistyakov A.E.

Open Access
|
Abstract
Introduction. Mathematical tools integrated with satellite data are typically employed as the primary means for studying aquatic ecosystems and forecasting changes in phytoplankton concentration in shallow water bodies during summer. This approach facilitates accurate monitoring, analysis, and modeling of the spatiotemporal dynamics of biogeochemical processes, considering the combined effects of various physicochemical, biological, and anthropogenic factors impacting the aquatic ecosystem. The authors have developed a mathematical model aligned with satellite data to predict the behavior of summer phytoplankton species in shallow water under accelerated temporal conditions. The model describes oxidative[1]reduction processes, sulfate reduction, and nutrient transformations (phytoplankton mineral nutrition), investigates hypoxia events caused by anthropogenic eutrophication, and forecasts changes in the oxygen and nutrient regimes of the water body.Materials and Methods. To simulate the population dynamics of summer phytoplankton species correlated with satellite data assimilation methods, an operational algorithm for restoring water quality parameters of the Azov Sea was developed based on the Levenberg-Marquardt multidimensional optimization method. The initial distribution of phytoplankton populations was obtained by applying the Local Binary Patterns (LBP) method to satellite images of the Taganrog Bay and was used as input data for the mathematical model.Results. Using integrated hydrodynamic and biological kinetics models combined with satellite data assimilation methods, a software suite was developed. This suite enables short- and medium-term forecasts of the ecological state of shallow water bodies based on diverse input data correlated with satellite information.Discussion and Conclusion. The conducted studies on aquatic systems revealed that improving the accuracy of initial data is one mechanism for enhancing the quality of biogeochemical process forecasting in marine ecosystems. It was established that using satellite data alongside mathematical modeling methods allows for studying the spatiotemporal distribution of pollutants of various origins, plankton populations in the studied water body, and assessing the nature and scale of natural or anthropogenic phenomena to prevent negative economic and social consequences.

Increasing the Accuracy of Solving Boundary Value Problems with Linear Ordinary Differential Equations Using the Bubnov-Galerkin Method
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2025
,
citations by CoLab: 0
,
Volosova N.К., Volosov K.A., Volosova A.K., Karlov M.I., Pastukhov D.F., Pastukhov Y.F.

Open Access
|
Abstract
Introduction. This study investigates the possibility of increasing the accuracy of numerically solving boundary value problems using the modified Bubnov-Galerkin method with a linear ordinary differential equation, where the coefficients and the right-hand side are continuous functions. The order of the differential equation n must be less than the number of coordinate functions m.Materials and Methods. A modified Petrov-Galerkin method was used to numerically solve the boundary value problem. It employs a system of linearly independent power-type basis functions on the interval [−1,1], each normalized by the unit Chebyshev norm. The system of linear algebraic equations includes only the linearly independent boundary conditions of the original problem.Results. For the first time, an integral quadrature formula with a 22nd order error was developed for a uniform grid. This formula is used to calculate the matrix elements and coefficients in the right-hand side of the system of linear algebraic equations, taking into account the scalar product of two functions based on the new quadrature formula. The study proves a theorem on the existence and uniqueness of a solution for boundary value problems with general non-separated conditions, provided that n linearly independent particular solutions of a homogeneous differential equation of order n are known.Discussion and Conclusion. The hydrodynamic problem in a viscous strong boundary layer with a third-order equation was precisely solved. The analytical solution was compared with its numerical counterpart, and the uniform norm of their difference did not exceed 5·10‒15. The formulas derived using the generalized Bubnov-Galerkin method may be useful for solving boundary value problems with linear ordinary differential equations of the third and higher orders.

Forecasting Drilling Mud Losses Using Python
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2025
,
citations by CoLab: 0
,
Kornilaev N.V., Koledina K.F.

Open Access
|
Abstract
Introduction. Drilling mud losses are among the most common complications encountered during well drilling. Forecasting these losses is a priority as it helps minimize drilling fluid wastage and prevent wellbore incidents. Mud loss events are primarily influenced by the geological properties of the formations being drilled. Understanding the relationship between mud loss occurrences and the geological characteristics of the formations has both fundamental and practical significance. Given the complexity of predicting mud loss probabilities using traditional mathematical models, this study aims to develop a machine-learning-based system to predict the probability of mud losses based on well location and stratigraphic description.Materials and Methods. Experimental data from 735 wells at the Shkapovskoye oil field, including well location coordinates, geological layer indices, and mud loss intensities, were prepared for computational analysis. The dataset was divided into training and testing subsets. The classification problem was addressed using four intensity classes with the following machine learning models: Decision Tree, Random Forest, and Linear Discriminant Analysis.Results. Predictions generated by the three models were compared against the experimental data in the test set. The evaluation metrics included accuracy and recall. All three models achieved an average prediction accuracy of 91%. Linear Discriminant Analysis was identified as the most accurate model.Discussion and Conclusion. High-accuracy predictions enable reliable forecasting of the probability and intensity of mud losses based on the location and stratigraphic description of new wells. The study presents three machine learning methods that demonstrated superior results in solving this problem.

A Modified Bubnov-Galerkin Method for Solving Boundary Value Problems with Linear Ordinary Differential Equations
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 1
,
Volosova N.K., Volosov K.A., Volosova A.K., Pastukhov D.F., Pastukhov Y.F.

Open Access
|
Abstract
Introduction. The paper considers the solution of boundary value problems on an interval for linear ordinary differential equations, in which the coefficients and the right-hand side are continuous functions. The conditions for the orthogonality of the residual equation to the coordinate functions are supplemented by a system of linearly independent boundary conditions. The number of coordinate functions m must exceed the order n of the differential equation. Materials and Methods. To numerically solve the boundary value problem, a system of linearly independent coordinate functions is proposed on a symmetric interval [−1,1], where each function has a unit Chebyshev’s norm. A modified Petrov-Galerkin method is applied, incorporating linearly independent boundary conditions from the original problem into the system of linear algebraic equations. An integral quadrature formula with twelfth-order error is used to compute the scalar product of two functions. Results. A criterion for the existence and uniqueness of a solution to the boundary value problem is obtained, provided that n linearly independent solutions of the homogeneous differential equation are known. Formulas are derived for the matrix coefficients and the coefficients of the right-hand side in the system of linear algebraic equations for the vector expansion of the solution in terms of the coordinate function system. These formulas are obtained for second- and third-order linear differential equations. The modified Bubnov-Galerkin method is formulated for differential equations of arbitrary order. Discussion and Conclusions. The derived formulas for the generalized Bubnov-Galerkin method may be useful for solving boundary value problems involving linear ordinary differential equations. Three boundary value problems with second- and third-order differential equations are numerically solved, with the uniform norm of the residual not exceeding 10–11.

Construction of Second-Order Finite Difference Schemes for Diffusion-Convection Problems of Multifractional Suspensions in Coastal Marine Systems
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 0
,
Sidoryakina V.V.

Open Access
|
Abstract
Introduction. This paper addresses an initial-boundary value problem for the transport of multifractional suspensions applied to coastal marine systems. This problem describes the processes of transport, deposition of suspension particles, and the transitions between its various fractions. To obtain monotonic finite difference schemes for diffusion-convection problems of suspensions, it is advisable to use schemes that satisfy the maximum principle. When constructing a finite difference scheme that adheres to the maximum principle, it is desirable to achieve second-order spatial accuracy for bothinterior and boundary points of the domain under study. Materials and Methods. This problem presents certain difficulties when considering the boundaries of the geometric domain, where boundary conditions of the second and third kinds are applied. In these cases, to maintain second-order approximation accuracy, an “extended” grid is introduced (a grid supplemented with fictitious nodes). The guidelineis the approximation of the given boundary conditions using the central difference formula, with the exclusion of the concentration function at the fictitious node from the resulting expressions. Results. Second-order accurate finite difference schemes for the diffusion-convection problem of multifractional suspensions in coastal marine systems are constructed. Discussion and Conclusion. The proposed schemes are not absolutely stable, and a detailed analysis of stability and convergence, particularly concerning the grid step ratio, remains an important problem that the author plans to address in the future.

Mathematical Modelling of Spatially Inhomogeneous Non-Stationary Interaction of Pests with Transgenic and Non-Modified Crops Considering Taxis
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 0
,
Sukhinov A.I., Bugaeva I.A.

Open Access
|
Abstract
Introduction. This paper addresses a unified spatially inhomogeneous, non-stationary model of interaction between genetically modified crop resources (corn) and the corn borer pest, which is also present on a relatively small section of non-modified corn. The model assumes that insect pests influence both types of crops and are capable of independent movement (taxis) towards the gradient of plant resources. It also considers diffusion processes in the dynamics of all components of the unified model, biomass growth, genetic characteristics of both types of plant resources, processes of crop consumption, phenomena of growth and degradation, diffusion, and mutation of pests. The model allows for predictive calculations aimed at reducing crop losses and increasing the resistance of transgenic crops to pests by slowing down the natural mutation rate of the pest. Materials and Methods. The mathematical model is an extension of Kostitsin’s model and is formulated as an initial-boundary value problem for a nonlinear system of convection-diffusion equations. These equations describe the spatiotemporal dynamics of biomass density changes in two types of crops — transgenic and non-modified — as well as the specific populations (densities) of three genotypes of pests (the corn borer) resulting from mutations. The authors linearized the convection-diffusion equations by applying a time-lag method on the time grid, with nonlinear terms from eachequation taken from the previous time layer. The terms describing taxis are presented in a symmetric form, ensuring the skew-symmetry of the corresponding continuous operator and, in the case of spatial grid approximation, the finite-difference operator. Results. A stable monotonic finite-difference scheme is developed, approximating the original problem with second-order accuracy on a uniform 2D spatial grid. Numerical solutions of model problems are provided, qualitatively corresponding to observed processes. Solutions are obtained for various ratios of modified and non-modified sections of the field. Discussion and Conclusion. The obtained results regarding pest behavior, depending on the type of taxis, could significantly extend the time for pests to acquire Bt resistance. The concentration dynamics of pests moving in the direction of the food gradient differs markedly from the concentration of pests moving towards a mate for reproduction.

Mathematical Modelling of the Impact of IR Laser Radiation on an Oncoming Flow of Nanoparticles with Methane
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 0
,
Peskova E.E., Snytnikov V.N.

Open Access
|
Abstract
Introduction. The study is devoted to the numerical investigation of laser radiation’s effect on an oncoming two-phase flow of nanoparticles and multicomponent hydrocarbon gases. Under such exposure, the hydrogen content in the products increases, and methane is bound into more complex hydrocarbons on the surface of catalytic nanoparticles and in the gas phase. The hot walls of the tube serve as the primary source of heat for the reactive two-phase medium containing catalytic nanoparticles. Materials and Methods. The main method used is mathematical modelling, which includes the numerical solution of a system of equations for a viscous gas-dust two-phase medium, taking into account chemical reactions and laser radiation. The model accounts for the two-phase gas-dust medium’s multicomponent and multi-temperature nature, ordinary differential equations (ODEs) for the temperature of catalytic nanoparticles, ODEs of chemical kinetics, endothermic effects of radical chain reactions, diffusion of light methyl radicals CH3 and hydrogen atoms H, which initiate methaneconversion, as well as absorption of laser radiation by ethylene and particles. Results. The distributions of parameters characterizing laminar subsonic flows of the gas-dust medium in an axisymmetric tube with chemical reactions have been obtained. It is shown that the absorption of laser radiation by ethylene in the oncoming flow leads to a sharp increase in methane conversion and a predominance of aromatic compounds in the product output. Discussion and Conclusion. Numerical modelling of the dynamics of reactive two-phase media is of interest for the development of theoretical foundations for the processing of methane into valuable products. The results obtained confirm the need for joint use of mathematical modelling and laboratory experiments in the development of new resource-saving and economically viable technologies for natural gas processing.

Modelling of Capillary Discharge in Repetition Mode for Short Capillary Systems with Various Filling Methods
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 0
,
Gasilov V.A., Savenko N.O., Sharova Y.S.

Open Access
|
Abstract
Introduction. Currently, frequency modes of operation of electron accelerators based on capillary discharges are actively investigated. Electrons in these systems are accelerated by femtosecond laser pulses passing through the discharge plasma.Materials and Methods. The paper presents results of three-dimensional magnetohydrodynamic modelling of the capillary discharge cycle, including stages of filling a short capillary with working gas (hydrogen), formation of the plasma channel, and restoration of the working medium before the start of the next discharge. Calculations were performed assuming the system is under external cooling, which maintains thermal balance at intermediate stages of the working cycle, and under constant conditions of gas supply and evacuation.Results. The computational experiments demonstrate the capability of generating beams of relativistic electrons with a repetition frequency of approximately one kilohertz.Discussion and Conclusions. The obtained results allow us to speak about the prospects of using LWFA with a short channel length and a high repetition rate of the capillary discharge.

Application of Neural Networks to Solve the Dirichlet Problem for Areas of Complex
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 1
,
Galaburdin S.A.

Open Access
|
Abstract
Introduction. Many mathematical problems are reduced to solving partial differential equations (PDEs) in domains of complex shapes. Existing analytical and numerical methods do not always provide efficient solutions for such problems. Recently, neural networks have been successfully applied to solve PDEs, typically addressing boundary value problems for domains with simple shapes. This paper attempts to construct a neural network capable of effectively solving boundary value problems for domains of complex shapes.Materials and Methods. A method for constructing a neural network to solve the Dirichlet problem for regions of complex shape is proposed. Derivatives of singular solutions of the Laplace equation are accepted as activation functions. Singular points of these solutions are distributed along closed curves encompassing the boundary of the domain. The adjustment of the network weights is reduced to minimizing the root-mean-square error during training.Results. The results of solving Dirichlet problems for various complex-shaped domains are presented. The results are provided in tables, comparing the exact solution and the solution obtained using the neural network. Figures show the domain shapes and the locations of points where the solutions were determined.Discussion and Conclusion. The presented results indicate a good agreement between the obtained solution and the exact one. It is noted that this method can be easily applied to various boundary value problems. Methods for enhancing the efficiency of such neural networks are suggested.

Locating the Interface between Different Media Based on Matrix Ultrasonic Sensor Data Using Convolutional Neural Networks
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 0
,
Vasyukov A.V.

Open Access
|
Abstract
Introduction. The study focuses on modelling the process of ultrasound medical examination in a heterogeneous environment with regions of significantly different sound speeds. Such scenarios typically arise when visualizing brain structures through the skull. The aim of this work is to compare possible approaches to determining the interface between acoustically contrasting media using convolutional neural networks.Materials and Methods. Numerical modelling of the direct problem is performed, obtaining synthetic calculated ultrasonic images based on known geometry and rheology of the area as well as sensor parameters. The calculated images reproduce distortions and artifacts typical for setups involving the skull wall. Convolutional neural networks of 2D and 3D structures following the UNet architecture are used to solve the inverse problem of determining the interface between media based on a sensor signal. The networks are trained on computational datasets and then tested on individual samples not used in training.Results. Numerical B-scans for characteristic setups were obtained. The possibility of localizing the aberrator boundary with good quality for both 2D and 3D convolutional networks was demonstrated. A higher quality result was obtained for the 3D network in the presence of significant noise and artifacts in the input data. It was established that the 3D architecture network can provide the shape of the interface between media in 0.1 seconds.Discussion and Conclusions. The results can be used for the development of transcranial ultrasound technologies. Rapid localization of the skull boundary can be incorporated into imaging algorithms to compensate for distortions caused by differences in sound velocities in bone and soft tissues.

Probabilistic Analysis of Heat Flux Distribution in the North Atlantic for 1979‒2022
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 0
,
Belyaev K.P., Kuleshov A.A., Novikova A.V., Tuchkova N.P.

Open Access
|
Abstract
Introduction. The study of heat interaction processes and the distribution of heat flaxes in the oceans is important for understanding climate change on Earth. The North Atlantic, which is one of the key components of the global climate system, plays a significant role in regulating the climate of our latitudes. One of the key tools for analyzing heat distribution in the oceans is probabilistic analysis. In this work, using mathematical modelling methods, a statistical analysis of observational data on heat fluxes in the North Atlantic is carried out.Materials and Methods. The used methods include the analysis of random processes specified by the stochastic differential equation (SDE) or the Ito equation, approximation of observational data, and solution of the Fokker-Planck-Kolmogorov (FPK) equation to describe the evolution of the probabilistic distribution of heat in the ocean.Results. Using mathematical modelling methods, a probabilistic analysis of the distribution of heat fluxes in the North Atlantic for the period from 1979 to 2022 has been carried out. The results of the study made it possible to establish patterns of distribution of heat flux in the studied region over the period of time under consideration.Discussion and Conclusions. The results may be useful for further study of climate processes in the North Atlantic, as well as for the development of resource management and environmental protection strategies.

Mathematical Modelling of Catastrophic Surge and Seiche Events in the Azov Sea Using Remote Sensing Data
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 1
,
Protsenko E.A., Panasenko N.D., Protsenko S.V.

Open Access
|
Abstract
Introduction. This work is devoted to the mathematical modelling of extreme sea level fluctuations in the Azov Sea using remote sensing data. The aim of the study is to develop and apply a mathematical model that allows more accurate prediction of surge and seiche events caused by extreme wind conditions. The relevance of the work is due to the need to improve the forecasts of hydrodynamic processes in shallow water bodies (such as the Azov Sea), where such phenomena can have significant economic and ecological consequences. The goal of this work is to develop and apply a mathematical model for predicting extreme sea level fluctuations in the Azov Sea caused by wind conditions.Materials and Methods. The study is based on the analysis of remote sensing data and observations of wind speed and direction over the Azov Sea. The primary method used is mathematical modelling, which includes solving the system of shallow water hydrodynamics equations. Wind condition data were collected from November 20 to 25, 2019, during which catastrophic sea level fluctuations were observed. The model considers the components of water flow velocity, water density, hydrodynamic pressure, gravitational acceleration, and turbulence exchange coefficients.Results. The modelling showed that prolonged easterly winds with speeds up to 22 m/s led to significant surge and seiche fluctuations in sea level. The maximum amplitudes of fluctuations were recorded in the central part of the Taganrog Bay, where the wind direction and speed remained almost constant throughout the observation period. Data from various platforms located in different parts of the Azov Sea confirmed a significant decrease in water level in the northeast and an increase in the southwest.Discussion and Conclusions. The study results confirm that using mathematical models in combination with remote sensing data allows more accurate predictions of extreme sea level fluctuations. This is important for developing measures to prevent and mitigate the consequences of surge and seiche events in coastal areas. In the future, it is necessary to improve models by including additional factors such as climate change and anthropogenic impact on the Azov Sea ecosystem.

An Adaptive Mesh Refinement Solver for Regularized Shallow Water Equations
COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES
,
2024
,
citations by CoLab: 0
,
But I.I., Kiryushina M.A., Elistratov S.A., Elizarova T.G., Tiniakov A.D.

Open Access
|
Abstract
Introduction. We present a novel adaptive mesh refinement (AMR) solver, SWqgdAMR, based on the open software platform AMReX. The new solver is grounded in regularized shallow water equations. This paper details the equations, their discretization, and implementation features within AMReX. The efficacy of SWqgdAMR is demonstrated through two test cases: a two-dimensional circular dam break (collapse of a liquid column) and the collapse of two liquid columns of different heights.Materials and Methods. The SWqgdAMR solver is developed to extend the applicability of regularized equations in problems requiring high computational power and adaptive grids. SWqgdAMR is the first solver based on the quasigas dynamic (QGD) algorithm within the AMReX framework. The implementation and validation of SWqgdAMR represent a crucial step towards the further expansion of the QGD software suite.Results. The AMReX-based shallow water equations solver SWqgdAMR with adaptive mesh refinement is described and tested in detail. Validation of SWqgdAMR involved two-dimensional problems: the breach of a cylindrical dam and the breach of two cylindrical dams of different heights. The presented solver demonstrated high efficiency, with the use of adaptive mesh refinement technology accelerating the computation by 56 times compared to a stationary grid calculation.Discussion and Conclusions. The algorithm can be expanded to include bathymetry, external forces (wind force, bottom friction, Coriolis forces), and the mobility of the shoreline during wetting and drying phases, as has been done in individual codes for regularized shallow water equations (RSWE). The current implementation of the QGD algorithm did not test the potential for parallel computing on graphical cores.
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Applied Catalysis B: Environmental
787 citations, 0.16%
|
|
Nano Energy
785 citations, 0.16%
|
|
Inorganic Materials
781 citations, 0.16%
|
|
Solar Energy Materials and Solar Cells
759 citations, 0.15%
|
|
Energies
758 citations, 0.15%
|
|
Angewandte Chemie
756 citations, 0.15%
|
|
Bulletin of Materials Science
737 citations, 0.15%
|
|
Nanomaterials
735 citations, 0.15%
|
|
ChemElectroChem
735 citations, 0.15%
|
|
Corrosion Science
734 citations, 0.15%
|
|
Small
731 citations, 0.15%
|
|
Polymers
714 citations, 0.14%
|
|
Journal of Energy Chemistry
712 citations, 0.14%
|
|
Journal of Colloid and Interface Science
709 citations, 0.14%
|
|
Industrial & Engineering Chemistry Research
672 citations, 0.13%
|
|
Journal of Electronic Materials
671 citations, 0.13%
|
|
Materials Today: Proceedings
661 citations, 0.13%
|
|
Angewandte Chemie - International Edition
653 citations, 0.13%
|
|
International Journal of Energy Research
652 citations, 0.13%
|
|
Progress in Materials Science
639 citations, 0.13%
|
|
Membranes
638 citations, 0.13%
|
|
Journal of Nuclear Materials
635 citations, 0.13%
|
|
Journal of Fuel Cell Science and Technology
635 citations, 0.13%
|
|
International Journal of Electrochemical Science
620 citations, 0.12%
|
|
Journal Physics D: Applied Physics
618 citations, 0.12%
|
|
Materials Science Forum
616 citations, 0.12%
|
|
Show all (70 more) | |
5000
10000
15000
20000
25000
30000
|
Citing publishers
50000
100000
150000
200000
250000
|
|
Elsevier
210784 citations, 42.22%
|
|
Springer Nature
49584 citations, 9.93%
|
|
Wiley
44299 citations, 8.87%
|
|
American Chemical Society (ACS)
41494 citations, 8.31%
|
|
Royal Society of Chemistry (RSC)
34503 citations, 6.91%
|
|
The Electrochemical Society
19075 citations, 3.82%
|
|
MDPI
10996 citations, 2.2%
|
|
Pleiades Publishing
7866 citations, 1.58%
|
|
IOP Publishing
7736 citations, 1.55%
|
|
Taylor & Francis
7322 citations, 1.47%
|
|
AIP Publishing
7268 citations, 1.46%
|
|
American Physical Society (APS)
4454 citations, 0.89%
|
|
Trans Tech Publications
4261 citations, 0.85%
|
|
Cambridge University Press
2005 citations, 0.4%
|
|
Walter de Gruyter
1891 citations, 0.38%
|
|
Institute of Electrical and Electronics Engineers (IEEE)
1798 citations, 0.36%
|
|
The Electrochemical Society of Japan
1689 citations, 0.34%
|
|
1595 citations, 0.32%
|
|
Ceramic Society of Japan
1593 citations, 0.32%
|
|
World Scientific
1146 citations, 0.23%
|
|
Frontiers Media S.A.
1052 citations, 0.21%
|
|
ASME International
1050 citations, 0.21%
|
|
Japan Society of Applied Physics
998 citations, 0.2%
|
|
Hindawi Limited
875 citations, 0.18%
|
|
Korean Ceramic Society
748 citations, 0.15%
|
|
Oxford University Press
659 citations, 0.13%
|
|
Physical Society of Japan
604 citations, 0.12%
|
|
SAGE
591 citations, 0.12%
|
|
Autonomous Non-profit Organization Editorial Board of the journal Uspekhi Khimii
511 citations, 0.1%
|
|
The Chemical Society of Japan
505 citations, 0.1%
|
|
492 citations, 0.1%
|
|
Chinese Society of Rare Earths
488 citations, 0.1%
|
|
Japan Institute of Metals
453 citations, 0.09%
|
|
Annual Reviews
434 citations, 0.09%
|
|
EDP Sciences
418 citations, 0.08%
|
|
International Union of Crystallography (IUCr)
415 citations, 0.08%
|
|
Korean Society of Industrial Engineering Chemistry
402 citations, 0.08%
|
|
American Association for the Advancement of Science (AAAS)
366 citations, 0.07%
|
|
Nonferrous Metals Society of China
346 citations, 0.07%
|
|
Tsinghua University Press
312 citations, 0.06%
|
|
American Vacuum Society
312 citations, 0.06%
|
|
Thomas Telford
309 citations, 0.06%
|
|
The Korean Electrochemical Society
267 citations, 0.05%
|
|
Acta Physica Sinica, Chinese Physical Society and Institute of Physics, Chinese Academy of Sciences
252 citations, 0.05%
|
|
Scientific Research Publishing
219 citations, 0.04%
|
|
The Royal Society
203 citations, 0.04%
|
|
Shanghai Institute of Ceramics
200 citations, 0.04%
|
|
Wuhan University of Technology
192 citations, 0.04%
|
|
Chinese Ceramic Society
189 citations, 0.04%
|
|
Associacao Brasileiro de Ceramica
180 citations, 0.04%
|
|
Ural Federal University
175 citations, 0.04%
|
|
Universidade Federal do Rio de Janeiro
171 citations, 0.03%
|
|
Optica Publishing Group
165 citations, 0.03%
|
|
King Saud University
156 citations, 0.03%
|
|
The Russian Academy of Sciences
156 citations, 0.03%
|
|
Polymer Society of Korea
155 citations, 0.03%
|
|
IntechOpen
155 citations, 0.03%
|
|
Japan Society of Powder and Powder Metallurgy
147 citations, 0.03%
|
|
Korean Institute of Metals and Materials
143 citations, 0.03%
|
|
University of Science and Technology Beijing
133 citations, 0.03%
|
|
OAE Publishing Inc.
129 citations, 0.03%
|
|
Japanese Association of Inorganic Phosphorus Chemistry
128 citations, 0.03%
|
|
Taiwan Institute of Chemical Engineers
124 citations, 0.02%
|
|
Sociedad Espanola de Ceramica y Vidrio
123 citations, 0.02%
|
|
SAE International
121 citations, 0.02%
|
|
Universidade Federal de São Carlos
114 citations, 0.02%
|
|
National Academy of Sciences of Ukraine - Institute of Semiconductor Physics
112 citations, 0.02%
|
|
Research Square Platform LLC
111 citations, 0.02%
|
|
Proceedings of the National Academy of Sciences (PNAS)
106 citations, 0.02%
|
|
Society of Chemical Engineers, Japan
99 citations, 0.02%
|
|
Bentham Science Publishers Ltd.
95 citations, 0.02%
|
|
Copernicus
94 citations, 0.02%
|
|
Institute of Physics, Polish Academy of Sciences
94 citations, 0.02%
|
|
ASM International
87 citations, 0.02%
|
|
Beilstein-Institut
87 citations, 0.02%
|
|
Institution of Engineering and Technology (IET)
86 citations, 0.02%
|
|
The Materials Research Society of Japan
85 citations, 0.02%
|
|
The Surface Science Society of Japan
85 citations, 0.02%
|
|
Higher Education Press
83 citations, 0.02%
|
|
78 citations, 0.02%
|
|
SPIE-Intl Soc Optical Eng
77 citations, 0.02%
|
|
IGI Global
75 citations, 0.02%
|
|
Canadian Science Publishing
74 citations, 0.01%
|
|
Iron and Steel Institute of Japan
73 citations, 0.01%
|
|
Science in China Press
70 citations, 0.01%
|
|
Social Science Electronic Publishing
70 citations, 0.01%
|
|
Emerald
69 citations, 0.01%
|
|
China Science Publishing & Media
69 citations, 0.01%
|
|
The Crystallographic Society of Japan
67 citations, 0.01%
|
|
66 citations, 0.01%
|
|
Japan Society of Mechanical Engineers
65 citations, 0.01%
|
|
National Academy of Sciences of Ukraine (Co. LTD Ukrinformnauka) (Publications)
65 citations, 0.01%
|
|
64 citations, 0.01%
|
|
Hans Publishers
63 citations, 0.01%
|
|
National Library of Serbia
60 citations, 0.01%
|
|
55 citations, 0.01%
|
|
Lithuanian Physical Society
53 citations, 0.01%
|
|
American Institute of Mathematical Sciences (AIMS)
52 citations, 0.01%
|
|
The Surface Finishing Society of Japan
50 citations, 0.01%
|
|
Society of Powder Technology
48 citations, 0.01%
|
|
Show all (70 more) | |
50000
100000
150000
200000
250000
|
Publishing organizations
50
100
150
200
250
300
|
|
Tohoku University
263 publications, 1.6%
|
|
National Institute of Advanced Industrial Science and Technology
181 publications, 1.1%
|
|
Max Planck Institute for Solid State Research
159 publications, 0.97%
|
|
Tokyo Institute of Technology
155 publications, 0.94%
|
|
Kyoto University
146 publications, 0.89%
|
|
Imperial College London
131 publications, 0.8%
|
|
Warsaw University of Technology
128 publications, 0.78%
|
|
University of Tokyo
126 publications, 0.77%
|
|
National Institute for Materials Science
115 publications, 0.7%
|
|
Forschungszentrum Jülich
114 publications, 0.69%
|
|
Kyushu University
110 publications, 0.67%
|
|
Chalmers University of Technology
108 publications, 0.66%
|
|
Ural Federal University
107 publications, 0.65%
|
|
Nagoya University
105 publications, 0.64%
|
|
Technical University of Denmark
101 publications, 0.61%
|
|
Osaka Metropolitan University
99 publications, 0.6%
|
|
AGH University of Krakow
95 publications, 0.58%
|
|
Osaka University
92 publications, 0.56%
|
|
University of Oslo
88 publications, 0.53%
|
|
University of Bordeaux
83 publications, 0.5%
|
|
Shanghai Institute of Ceramics, Chinese Academy of Sciences
80 publications, 0.49%
|
|
Institute of Solid State Chemistry and Mechanochemistry of the Siberian Branch of the Russian Academy of Sciences
74 publications, 0.45%
|
|
Uppsala University
73 publications, 0.44%
|
|
National University of Singapore
69 publications, 0.42%
|
|
Tokyo University of Science
67 publications, 0.41%
|
|
University of Pennsylvania
66 publications, 0.4%
|
|
Technion – Israel Institute of Technology
65 publications, 0.4%
|
|
Nagoya Institute of Technology
65 publications, 0.4%
|
|
University of Science and Technology of China
65 publications, 0.4%
|
|
University of St Andrews
65 publications, 0.4%
|
|
University of Twente
64 publications, 0.39%
|
|
University of Münster
62 publications, 0.38%
|
|
Institute of High Temperature Electrochemistry of the Ural Branch of the Russian Academy of Sciences
61 publications, 0.37%
|
|
Universidad Complutense de Madrid
61 publications, 0.37%
|
|
Northwestern University
60 publications, 0.36%
|
|
Central South University
58 publications, 0.35%
|
|
Massachusetts Institute of Technology
58 publications, 0.35%
|
|
Oak Ridge National Laboratory
57 publications, 0.35%
|
|
Seoul National University
56 publications, 0.34%
|
|
McMaster University
56 publications, 0.34%
|
|
Hokkaido University
56 publications, 0.34%
|
|
Argonne National Laboratory
55 publications, 0.33%
|
|
Institute of Solid State Chemistry of the Ural Branch of the Russian Academy of Sciences
54 publications, 0.33%
|
|
Karlsruhe Institute of Technology
54 publications, 0.33%
|
|
Sorbonne University
54 publications, 0.33%
|
|
University of Aberdeen
50 publications, 0.3%
|
|
Delft University of Technology
47 publications, 0.29%
|
|
Université Paris-Saclay
47 publications, 0.29%
|
|
Université de Lille
47 publications, 0.29%
|
|
Lomonosov Moscow State University
46 publications, 0.28%
|
|
Stanford University
46 publications, 0.28%
|
|
Vienna University of Technology
46 publications, 0.28%
|
|
Novosibirsk State University
45 publications, 0.27%
|
|
Shubnikov Institute of Crystallography
45 publications, 0.27%
|
|
Tsinghua University
45 publications, 0.27%
|
|
Sapienza University of Rome
45 publications, 0.27%
|
|
University of Science and Technology Beijing
45 publications, 0.27%
|
|
Niigata University
45 publications, 0.27%
|
|
Grenoble Alpes University
43 publications, 0.26%
|
|
Doshisha University
43 publications, 0.26%
|
|
Indian Institute of Science
42 publications, 0.26%
|
|
Korea Institute of Science and Technology
42 publications, 0.26%
|
|
Harbin Institute of Technology
41 publications, 0.25%
|
|
Leibniz University Hannover
41 publications, 0.25%
|
|
University of Chinese Academy of Sciences
40 publications, 0.24%
|
|
Lawrence Berkeley National Laboratory
40 publications, 0.24%
|
|
Technical University of Darmstadt
40 publications, 0.24%
|
|
Clausthal University of Technology
40 publications, 0.24%
|
|
University of Pavia
39 publications, 0.24%
|
|
![]() Boreskov Institute of Catalysis of the Siberian Branch of the Russian Academy of Sciences
38 publications, 0.23%
|
|
Justus Liebig University Giessen
38 publications, 0.23%
|
|
Toyohashi University of Technology
38 publications, 0.23%
|
|
ETH Zurich
37 publications, 0.22%
|
|
Graz University of Technology
37 publications, 0.22%
|
|
Utrecht University
37 publications, 0.22%
|
|
Ibaraki University
37 publications, 0.22%
|
|
Jilin University
36 publications, 0.22%
|
|
Wuhan University of Technology
36 publications, 0.22%
|
|
RWTH Aachen University
36 publications, 0.22%
|
|
Mie University
36 publications, 0.22%
|
|
Pohang University of Science and Technology
35 publications, 0.21%
|
|
Institut Laue-Langevin
35 publications, 0.21%
|
|
Rutherford Appleton Laboratory
35 publications, 0.21%
|
|
Purdue University
35 publications, 0.21%
|
|
Queen Mary University of London
34 publications, 0.21%
|
|
University of California, Berkeley
34 publications, 0.21%
|
|
Japan Atomic Energy Agency
34 publications, 0.21%
|
|
University of Perugia
33 publications, 0.2%
|
|
Saint Petersburg State University
32 publications, 0.19%
|
|
University of Oxford
32 publications, 0.19%
|
|
Gdańsk University of Technology
32 publications, 0.19%
|
|
Australian Nuclear Science & Technology Organisation
31 publications, 0.19%
|
|
Arizona State University
31 publications, 0.19%
|
|
Federal Research Center of Problem of Chemical Physics and Medicinal Chemistry RAS
30 publications, 0.18%
|
|
Norwegian University of Science and Technology
30 publications, 0.18%
|
|
Institute of Physics, Chinese Academy of Sciences
30 publications, 0.18%
|
|
Technische Universität Dresden
29 publications, 0.18%
|
|
High Energy Accelerator Research Organization
29 publications, 0.18%
|
|
Okayama University
29 publications, 0.18%
|
|
University of Hyogo
29 publications, 0.18%
|
|
Show all (70 more) | |
50
100
150
200
250
300
|
Publishing organizations in 5 years
5
10
15
20
|
|
Ural Federal University
20 publications, 1.88%
|
|
Kyoto University
18 publications, 1.69%
|
|
National Institute of Advanced Industrial Science and Technology
17 publications, 1.6%
|
|
Central South University
15 publications, 1.41%
|
|
Institute of High Temperature Electrochemistry of the Ural Branch of the Russian Academy of Sciences
13 publications, 1.22%
|
|
Harbin Institute of Technology
12 publications, 1.13%
|
|
Tohoku University
12 publications, 1.13%
|
|
Forschungszentrum Jülich
12 publications, 1.13%
|
|
Osaka Metropolitan University
11 publications, 1.04%
|
|
Institute of Solid State Chemistry and Mechanochemistry of the Siberian Branch of the Russian Academy of Sciences
10 publications, 0.94%
|
|
Institute of Solid State Chemistry of the Ural Branch of the Russian Academy of Sciences
9 publications, 0.85%
|
|
Tianjin University
9 publications, 0.85%
|
|
Huazhong University of Science and Technology
8 publications, 0.75%
|
|
Soochow University (Suzhou)
8 publications, 0.75%
|
|
Max Planck Institute for Solid State Research
8 publications, 0.75%
|
|
Grenoble Alpes University
7 publications, 0.66%
|
|
University of Science and Technology Beijing
7 publications, 0.66%
|
|
University of Oslo
7 publications, 0.66%
|
|
Technical University of Denmark
7 publications, 0.66%
|
|
Graz University of Technology
7 publications, 0.66%
|
|
Guilin University of Technology
7 publications, 0.66%
|
|
University of Tokyo
7 publications, 0.66%
|
|
Toyohashi University of Technology
7 publications, 0.66%
|
|
University of Chinese Academy of Sciences
6 publications, 0.56%
|
|
South China University of Technology
6 publications, 0.56%
|
|
University of Electronic Science and Technology of China
6 publications, 0.56%
|
|
Nanchang University
6 publications, 0.56%
|
|
Shenyang University of Technology
6 publications, 0.56%
|
|
Tokyo Institute of Technology
6 publications, 0.56%
|
|
Zhengzhou University
6 publications, 0.56%
|
|
Vienna University of Technology
6 publications, 0.56%
|
|
Nagoya Institute of Technology
6 publications, 0.56%
|
|
Clausthal University of Technology
6 publications, 0.56%
|
|
M.N. Mikheev Institute of Metal Physics of the Ural Branch of the Russian Academy of Sciences
5 publications, 0.47%
|
|
Novosibirsk State University
5 publications, 0.47%
|
|
Federal Research Center of Problem of Chemical Physics and Medicinal Chemistry RAS
5 publications, 0.47%
|
|
Gebze Technical University
5 publications, 0.47%
|
|
Tsinghua University
5 publications, 0.47%
|
|
Zhejiang University
5 publications, 0.47%
|
|
Wuhan University of Technology
5 publications, 0.47%
|
|
Northeastern University
5 publications, 0.47%
|
|
National Institute for Materials Science
5 publications, 0.47%
|
|
Shanghai University
5 publications, 0.47%
|
|
Jiangsu University of Science and Technology
5 publications, 0.47%
|
|
Southern University of Science and Technology
5 publications, 0.47%
|
|
Institute of Physics, Chinese Academy of Sciences
5 publications, 0.47%
|
|
Lawrence Berkeley National Laboratory
5 publications, 0.47%
|
|
University of Birmingham
5 publications, 0.47%
|
|
Guizhou University
5 publications, 0.47%
|
|
Chonnam National University
5 publications, 0.47%
|
|
Kunming University of Science and Technology
5 publications, 0.47%
|
|
Jiangxi University of Science and Technology
5 publications, 0.47%
|
|
University of Science and Technology of China
5 publications, 0.47%
|
|
Kyushu University
5 publications, 0.47%
|
|
Lomonosov Moscow State University
4 publications, 0.38%
|
|
Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences
4 publications, 0.38%
|
|
Skolkovo Institute of Science and Technology
4 publications, 0.38%
|
|
Boreskov Institute of Catalysis of the Siberian Branch of the Russian Academy of Sciences
4 publications, 0.38%
|
|
Samara State Technical University
4 publications, 0.38%
|
|
Tananaev Institute of Chemistry of the Kola Science Centre of the Russian Academy of Sciences
4 publications, 0.38%
|
|
Kola Science Center of the Russian Academy of Sciences
4 publications, 0.38%
|
|
King Saud University
4 publications, 0.38%
|
|
Indian Institute of Technology Roorkee
4 publications, 0.38%
|
|
Shanghai Jiao Tong University
4 publications, 0.38%
|
|
Northwestern Polytechnical University
4 publications, 0.38%
|
|
Karlsruhe Institute of Technology
4 publications, 0.38%
|
|
China University of Mining and Technology
4 publications, 0.38%
|
|
Nanjing University of Science and Technology
4 publications, 0.38%
|
|
Nanjing Tech University
4 publications, 0.38%
|
|
Wuhan University
4 publications, 0.38%
|
|
Aalto University
4 publications, 0.38%
|
|
Sun Yat-sen University
4 publications, 0.38%
|
|
South China Normal University
4 publications, 0.38%
|
|
Xiamen University
4 publications, 0.38%
|
|
Taiyuan University of Technology
4 publications, 0.38%
|
|
Yangtze University
4 publications, 0.38%
|
|
Shenyang University of Chemical Technology
4 publications, 0.38%
|
|
Sorbonne University
4 publications, 0.38%
|
|
Massachusetts Institute of Technology
4 publications, 0.38%
|
|
National University of Singapore
4 publications, 0.38%
|
|
University of Tsukuba
4 publications, 0.38%
|
|
Qilu University of Technology
4 publications, 0.38%
|
|
University of California, Berkeley
4 publications, 0.38%
|
|
Henan University
4 publications, 0.38%
|
|
Xiangtan University
4 publications, 0.38%
|
|
Oak Ridge National Laboratory
4 publications, 0.38%
|
|
Lanzhou University of Technology
4 publications, 0.38%
|
|
Jingdezhen Ceramic University
4 publications, 0.38%
|
|
Helmholtz-Institute Münster
4 publications, 0.38%
|
|
RWTH Aachen University
4 publications, 0.38%
|
|
Technical University of Braunschweig
4 publications, 0.38%
|
|
Hokkaido University
4 publications, 0.38%
|
|
Japan Synchrotron Radiation Research Institute
4 publications, 0.38%
|
|
AGH University of Krakow
4 publications, 0.38%
|
|
Warsaw University of Technology
4 publications, 0.38%
|
|
Polytechnic University of Valencia
4 publications, 0.38%
|
|
University of Utah
4 publications, 0.38%
|
|
Université Paris-Saclay
4 publications, 0.38%
|
|
Moscow Institute of Physics and Technology
3 publications, 0.28%
|
|
Nikolaev Institute of Inorganic Chemistry of the Siberian Branch of the Russian Academy of Sciences
3 publications, 0.28%
|
|
Show all (70 more) | |
5
10
15
20
|
Publishing countries
500
1000
1500
2000
2500
|
|
Japan
|
Japan, 2084, 12.66%
Japan
2084 publications, 12.66%
|
USA
|
USA, 1454, 8.84%
USA
1454 publications, 8.84%
|
China
|
China, 1421, 8.64%
China
1421 publications, 8.64%
|
Germany
|
Germany, 1000, 6.08%
Germany
1000 publications, 6.08%
|
France
|
France, 954, 5.8%
France
954 publications, 5.8%
|
United Kingdom
|
United Kingdom, 639, 3.88%
United Kingdom
639 publications, 3.88%
|
Russia
|
Russia, 504, 3.06%
Russia
504 publications, 3.06%
|
India
|
India, 434, 2.64%
India
434 publications, 2.64%
|
Poland
|
Poland, 419, 2.55%
Poland
419 publications, 2.55%
|
Republic of Korea
|
Republic of Korea, 367, 2.23%
Republic of Korea
367 publications, 2.23%
|
Spain
|
Spain, 325, 1.98%
Spain
325 publications, 1.98%
|
Italy
|
Italy, 322, 1.96%
Italy
322 publications, 1.96%
|
Sweden
|
Sweden, 236, 1.43%
Sweden
236 publications, 1.43%
|
Canada
|
Canada, 230, 1.4%
Canada
230 publications, 1.4%
|
Netherlands
|
Netherlands, 205, 1.25%
Netherlands
205 publications, 1.25%
|
Denmark
|
Denmark, 189, 1.15%
Denmark
189 publications, 1.15%
|
Australia
|
Australia, 137, 0.83%
Australia
137 publications, 0.83%
|
Norway
|
Norway, 122, 0.74%
Norway
122 publications, 0.74%
|
Switzerland
|
Switzerland, 121, 0.74%
Switzerland
121 publications, 0.74%
|
Austria
|
Austria, 120, 0.73%
Austria
120 publications, 0.73%
|
Portugal
|
Portugal, 118, 0.72%
Portugal
118 publications, 0.72%
|
Israel
|
Israel, 109, 0.66%
Israel
109 publications, 0.66%
|
Greece
|
Greece, 102, 0.62%
Greece
102 publications, 0.62%
|
Singapore
|
Singapore, 92, 0.56%
Singapore
92 publications, 0.56%
|
Brazil
|
Brazil, 84, 0.51%
Brazil
84 publications, 0.51%
|
Lithuania
|
Lithuania, 64, 0.39%
Lithuania
64 publications, 0.39%
|
Argentina
|
Argentina, 56, 0.34%
Argentina
56 publications, 0.34%
|
Turkey
|
Turkey, 56, 0.34%
Turkey
56 publications, 0.34%
|
Belgium
|
Belgium, 50, 0.3%
Belgium
50 publications, 0.3%
|
Iran
|
Iran, 49, 0.3%
Iran
49 publications, 0.3%
|
Ukraine
|
Ukraine, 48, 0.29%
Ukraine
48 publications, 0.29%
|
Egypt
|
Egypt, 43, 0.26%
Egypt
43 publications, 0.26%
|
Belarus
|
Belarus, 41, 0.25%
Belarus
41 publications, 0.25%
|
USSR
|
USSR, 41, 0.25%
USSR
41 publications, 0.25%
|
Latvia
|
Latvia, 38, 0.23%
Latvia
38 publications, 0.23%
|
Hungary
|
Hungary, 35, 0.21%
Hungary
35 publications, 0.21%
|
Czech Republic
|
Czech Republic, 33, 0.2%
Czech Republic
33 publications, 0.2%
|
Mexico
|
Mexico, 31, 0.19%
Mexico
31 publications, 0.19%
|
Malaysia
|
Malaysia, 29, 0.18%
Malaysia
29 publications, 0.18%
|
Slovakia
|
Slovakia, 28, 0.17%
Slovakia
28 publications, 0.17%
|
Finland
|
Finland, 28, 0.17%
Finland
28 publications, 0.17%
|
Yugoslavia
|
Yugoslavia, 27, 0.16%
Yugoslavia
27 publications, 0.16%
|
Colombia
|
Colombia, 26, 0.16%
Colombia
26 publications, 0.16%
|
Slovenia
|
Slovenia, 26, 0.16%
Slovenia
26 publications, 0.16%
|
Sri Lanka
|
Sri Lanka, 25, 0.15%
Sri Lanka
25 publications, 0.15%
|
Bulgaria
|
Bulgaria, 24, 0.15%
Bulgaria
24 publications, 0.15%
|
Tunisia
|
Tunisia, 24, 0.15%
Tunisia
24 publications, 0.15%
|
Morocco
|
Morocco, 22, 0.13%
Morocco
22 publications, 0.13%
|
Thailand
|
Thailand, 20, 0.12%
Thailand
20 publications, 0.12%
|
Serbia
|
Serbia, 19, 0.12%
Serbia
19 publications, 0.12%
|
South Africa
|
South Africa, 19, 0.12%
South Africa
19 publications, 0.12%
|
New Zealand
|
New Zealand, 18, 0.11%
New Zealand
18 publications, 0.11%
|
Croatia
|
Croatia, 18, 0.11%
Croatia
18 publications, 0.11%
|
Pakistan
|
Pakistan, 16, 0.1%
Pakistan
16 publications, 0.1%
|
Saudi Arabia
|
Saudi Arabia, 16, 0.1%
Saudi Arabia
16 publications, 0.1%
|
Estonia
|
Estonia, 15, 0.09%
Estonia
15 publications, 0.09%
|
Algeria
|
Algeria, 11, 0.07%
Algeria
11 publications, 0.07%
|
Indonesia
|
Indonesia, 11, 0.07%
Indonesia
11 publications, 0.07%
|
Romania
|
Romania, 10, 0.06%
Romania
10 publications, 0.06%
|
Czechoslovakia
|
Czechoslovakia, 9, 0.05%
Czechoslovakia
9 publications, 0.05%
|
Ireland
|
Ireland, 7, 0.04%
Ireland
7 publications, 0.04%
|
Iraq
|
Iraq, 5, 0.03%
Iraq
5 publications, 0.03%
|
Montenegro
|
Montenegro, 5, 0.03%
Montenegro
5 publications, 0.03%
|
Kazakhstan
|
Kazakhstan, 4, 0.02%
Kazakhstan
4 publications, 0.02%
|
Vietnam
|
Vietnam, 4, 0.02%
Vietnam
4 publications, 0.02%
|
Cuba
|
Cuba, 4, 0.02%
Cuba
4 publications, 0.02%
|
Peru
|
Peru, 4, 0.02%
Peru
4 publications, 0.02%
|
Chile
|
Chile, 4, 0.02%
Chile
4 publications, 0.02%
|
Bangladesh
|
Bangladesh, 3, 0.02%
Bangladesh
3 publications, 0.02%
|
Venezuela
|
Venezuela, 3, 0.02%
Venezuela
3 publications, 0.02%
|
Libya
|
Libya, 3, 0.02%
Libya
3 publications, 0.02%
|
Nigeria
|
Nigeria, 3, 0.02%
Nigeria
3 publications, 0.02%
|
Uzbekistan
|
Uzbekistan, 3, 0.02%
Uzbekistan
3 publications, 0.02%
|
Uruguay
|
Uruguay, 3, 0.02%
Uruguay
3 publications, 0.02%
|
Jordan
|
Jordan, 2, 0.01%
Jordan
2 publications, 0.01%
|
Yemen
|
Yemen, 2, 0.01%
Yemen
2 publications, 0.01%
|
UAE
|
UAE, 2, 0.01%
UAE
2 publications, 0.01%
|
Rwanda
|
Rwanda, 2, 0.01%
Rwanda
2 publications, 0.01%
|
Ethiopia
|
Ethiopia, 2, 0.01%
Ethiopia
2 publications, 0.01%
|
Azerbaijan
|
Azerbaijan, 1, 0.01%
Azerbaijan
1 publication, 0.01%
|
Bahrain
|
Bahrain, 1, 0.01%
Bahrain
1 publication, 0.01%
|
Brunei
|
Brunei, 1, 0.01%
Brunei
1 publication, 0.01%
|
Georgia
|
Georgia, 1, 0.01%
Georgia
1 publication, 0.01%
|
Iceland
|
Iceland, 1, 0.01%
Iceland
1 publication, 0.01%
|
Kenya
|
Kenya, 1, 0.01%
Kenya
1 publication, 0.01%
|
North Korea
|
North Korea, 1, 0.01%
North Korea
1 publication, 0.01%
|
Mongolia
|
Mongolia, 1, 0.01%
Mongolia
1 publication, 0.01%
|
Nepal
|
Nepal, 1, 0.01%
Nepal
1 publication, 0.01%
|
Palestine
|
Palestine, 1, 0.01%
Palestine
1 publication, 0.01%
|
Puerto Rico
|
Puerto Rico, 1, 0.01%
Puerto Rico
1 publication, 0.01%
|
Show all (60 more) | |
500
1000
1500
2000
2500
|
Publishing countries in 5 years
50
100
150
200
250
300
350
|
|
China
|
China, 322, 30.32%
China
322 publications, 30.32%
|
Japan
|
Japan, 102, 9.6%
Japan
102 publications, 9.6%
|
Russia
|
Russia, 64, 6.03%
Russia
64 publications, 6.03%
|
USA
|
USA, 53, 4.99%
USA
53 publications, 4.99%
|
Germany
|
Germany, 52, 4.9%
Germany
52 publications, 4.9%
|
India
|
India, 48, 4.52%
India
48 publications, 4.52%
|
France
|
France, 32, 3.01%
France
32 publications, 3.01%
|
United Kingdom
|
United Kingdom, 24, 2.26%
United Kingdom
24 publications, 2.26%
|
Republic of Korea
|
Republic of Korea, 24, 2.26%
Republic of Korea
24 publications, 2.26%
|
Turkey
|
Turkey, 17, 1.6%
Turkey
17 publications, 1.6%
|
Austria
|
Austria, 16, 1.51%
Austria
16 publications, 1.51%
|
Iran
|
Iran, 14, 1.32%
Iran
14 publications, 1.32%
|
Spain
|
Spain, 14, 1.32%
Spain
14 publications, 1.32%
|
Italy
|
Italy, 14, 1.32%
Italy
14 publications, 1.32%
|
Poland
|
Poland, 14, 1.32%
Poland
14 publications, 1.32%
|
Brazil
|
Brazil, 10, 0.94%
Brazil
10 publications, 0.94%
|
Pakistan
|
Pakistan, 10, 0.94%
Pakistan
10 publications, 0.94%
|
Denmark
|
Denmark, 9, 0.85%
Denmark
9 publications, 0.85%
|
Saudi Arabia
|
Saudi Arabia, 9, 0.85%
Saudi Arabia
9 publications, 0.85%
|
Australia
|
Australia, 8, 0.75%
Australia
8 publications, 0.75%
|
Israel
|
Israel, 8, 0.75%
Israel
8 publications, 0.75%
|
Norway
|
Norway, 8, 0.75%
Norway
8 publications, 0.75%
|
Canada
|
Canada, 7, 0.66%
Canada
7 publications, 0.66%
|
Belgium
|
Belgium, 5, 0.47%
Belgium
5 publications, 0.47%
|
Mexico
|
Mexico, 5, 0.47%
Mexico
5 publications, 0.47%
|
Czech Republic
|
Czech Republic, 5, 0.47%
Czech Republic
5 publications, 0.47%
|
Argentina
|
Argentina, 4, 0.38%
Argentina
4 publications, 0.38%
|
Greece
|
Greece, 4, 0.38%
Greece
4 publications, 0.38%
|
Lithuania
|
Lithuania, 4, 0.38%
Lithuania
4 publications, 0.38%
|
Malaysia
|
Malaysia, 4, 0.38%
Malaysia
4 publications, 0.38%
|
Morocco
|
Morocco, 4, 0.38%
Morocco
4 publications, 0.38%
|
Singapore
|
Singapore, 4, 0.38%
Singapore
4 publications, 0.38%
|
Finland
|
Finland, 4, 0.38%
Finland
4 publications, 0.38%
|
Switzerland
|
Switzerland, 4, 0.38%
Switzerland
4 publications, 0.38%
|
Ukraine
|
Ukraine, 3, 0.28%
Ukraine
3 publications, 0.28%
|
Portugal
|
Portugal, 3, 0.28%
Portugal
3 publications, 0.28%
|
Sweden
|
Sweden, 3, 0.28%
Sweden
3 publications, 0.28%
|
Bangladesh
|
Bangladesh, 2, 0.19%
Bangladesh
2 publications, 0.19%
|
Indonesia
|
Indonesia, 2, 0.19%
Indonesia
2 publications, 0.19%
|
Ireland
|
Ireland, 2, 0.19%
Ireland
2 publications, 0.19%
|
Netherlands
|
Netherlands, 2, 0.19%
Netherlands
2 publications, 0.19%
|
Slovakia
|
Slovakia, 2, 0.19%
Slovakia
2 publications, 0.19%
|
Thailand
|
Thailand, 2, 0.19%
Thailand
2 publications, 0.19%
|
Kazakhstan
|
Kazakhstan, 1, 0.09%
Kazakhstan
1 publication, 0.09%
|
Azerbaijan
|
Azerbaijan, 1, 0.09%
Azerbaijan
1 publication, 0.09%
|
Algeria
|
Algeria, 1, 0.09%
Algeria
1 publication, 0.09%
|
Vietnam
|
Vietnam, 1, 0.09%
Vietnam
1 publication, 0.09%
|
Egypt
|
Egypt, 1, 0.09%
Egypt
1 publication, 0.09%
|
Jordan
|
Jordan, 1, 0.09%
Jordan
1 publication, 0.09%
|
Iraq
|
Iraq, 1, 0.09%
Iraq
1 publication, 0.09%
|
North Korea
|
North Korea, 1, 0.09%
North Korea
1 publication, 0.09%
|
Colombia
|
Colombia, 1, 0.09%
Colombia
1 publication, 0.09%
|
Cuba
|
Cuba, 1, 0.09%
Cuba
1 publication, 0.09%
|
Latvia
|
Latvia, 1, 0.09%
Latvia
1 publication, 0.09%
|
Mongolia
|
Mongolia, 1, 0.09%
Mongolia
1 publication, 0.09%
|
Nepal
|
Nepal, 1, 0.09%
Nepal
1 publication, 0.09%
|
Nigeria
|
Nigeria, 1, 0.09%
Nigeria
1 publication, 0.09%
|
New Zealand
|
New Zealand, 1, 0.09%
New Zealand
1 publication, 0.09%
|
UAE
|
UAE, 1, 0.09%
UAE
1 publication, 0.09%
|
Uzbekistan
|
Uzbekistan, 1, 0.09%
Uzbekistan
1 publication, 0.09%
|
Sri Lanka
|
Sri Lanka, 1, 0.09%
Sri Lanka
1 publication, 0.09%
|
South Africa
|
South Africa, 1, 0.09%
South Africa
1 publication, 0.09%
|
Show all (32 more) | |
50
100
150
200
250
300
350
|
21 profile journal articles
Shlyakhtina Anna

N.N. Semenov Federal Research Center for Chemical Physics of the Russian Academy of Sciences
99 publications,
1 452 citations
h-index: 21
21 profile journal articles
Ivanov-Schitz Alexey
DSc in Chemistry, Professor

Institute of Crystallography
71 publications,
853 citations
h-index: 15
20 profile journal articles
Yaroslavtsev Andrey
DSc in Chemistry, Professor, Full member of the Russian Academy of Sciences

Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences
428 publications,
6 804 citations
h-index: 41
10 profile journal articles
Marrero-Lopez David
PhD in Philosophy, Professor

University of Malaga
147 publications,
5 378 citations
h-index: 45
Research interests
Fuel cells
9 profile journal articles
Lyskov Nikolay

Federal Research Center of Problem of Chemical Physics and Medicinal Chemistry RAS
99 publications,
1 006 citations
h-index: 17
8 profile journal articles
Filonova Elena
🤝
PhD in Chemistry, Associate Professor

Ural Federal University
75 publications,
1 064 citations
h-index: 19
Research interests
Hydrogen energy
Scientometrics
7 profile journal articles
Antipov Evgeny
DSc in Chemistry, Professor, Associate member of the Russian Academy of Sciences

Lomonosov Moscow State University

Skolkovo Institute of Science and Technology
454 publications,
9 841 citations
h-index: 48
7 profile journal articles
Kabanova Natalia

Samara State Technical University

Kola Science Center of the Russian Academy of Sciences
32 publications,
593 citations
h-index: 12
5 profile journal articles
Stenina Irina

Kurnakov Institute of General and Inorganic Chemistry of the Russian Academy of Sciences
144 publications,
1 919 citations
h-index: 22
5 profile journal articles
Kuzmin Alexei
282 publications,
5 526 citations
h-index: 38