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Considered the application of physics-informed neural networks using multi layer perceptrons to solve Cauchy initial value problems in which the right-hand sides of the equation are continuous monotonically increasing, decreasing or oscillating functions. With the use of the computational experiments the influence of the construction of the approximate neural network solution, neural network structure, optimization algorithm and software implementation means on the learning process and the accuracy of the obtained solution is studied. The analysis of the efficiency of the most frequently used machine learning frameworks in software development with the programming languages Python and C# is carried out. It is shown that the use of C# language allows to reduce the time of neural networks training by 20–40%. The choice of different activation functions affects the learning process and the accuracy of the approximate solution. The most effective functions in the considered problems are sigmoid and hyperbolic tangent. The minimum of the loss function is achieved at the certain number of neurons of the hidden layer of a single-layer neural network for a fixed training time of the neural network model. It’s also mentioned that the complication of the network structure increasing the number of neurons does not improve the training results. At the same time, the size of the grid step between the points of the training sample, providing a minimum of the loss function, is almost the same for the considered Cauchy problems. Training single-layer neural networks, the Adam method and its modifications are the most effective to solve the optimization problems. Additionally, the application of twoand three-layer neural networks is considered. It is shown that in these cases it is reasonable to use the LBFGS algorithm, which, in comparison with the Adam method, in some cases requires much shorter training time achieving the same solution accuracy. The specificity of neural network training for Cauchy problems in which the solution is an oscillating function with monotonically decreasing amplitude is also investigated. For these problems, it is necessary to construct a neural network solution with variable weight coefficient rather than with constant one, which improves the solution in the grid cells located near by the end point of the solution interval.

For cognitive radio systems, it is important to use efficient algorithms that search for free channels that can be provided to secondary users. Therefore, this paper is devoted to improving the accuracy of prediction frequency resource occupancy of a cellular communication system using spatiotemporal radio environment maps. The formation of a radio environment map is implemented for the fourthgeneration cellular communication system Long-Term Evolution. Taking this into account, a model structure has been developed that includes data generation and allows training and testing of an artificial neural network to predict the occupancy of frequency resources presented as the contents of radio environment map cells. A method for assessing prediction accuracy is described. The simulation model of the cellular communication system is implemented in the MatLab. The developed frequency resource occupancy prediction model is implemented in the Python. The complete file structure of the model is presented. The experiments were performed using artificial neural networks based on the Long Short-Term Memory and Kolmogorov – Arnold neural network architectures, taking into account its modification. It was found that with an equal number of parameters, the Kolmogorov –Arnold neural network learns faster for a given task. The obtained research results indicate an increase in the accuracy of prediction the occupancy of the frequency resource of the cellular communication system when using the Kolmogorov – Arnold neural network.

The paper constructs and studies a generalized model describing two-component systems of reaction – diffusion type with power nonlinearity, considering the influence of external noise. A methodology has been developed for analyzing the generalized model, which includes linear stability analysis, nonlinear stability analysis, and numerical simulation of the system’s evolution. The linear analysis technique uses basic approaches, in which the characteristic equation is obtained using a linearization matrix. Nonlinear stability analysis realized up to third-order moments inclusively. For this, the functions describing the dynamics of the components are expanded in Taylor series up to third-order terms. Then, using the Novikov theorem, the averaging procedure is carried out. As a result, the obtained equations form an infinite hierarchically subordinate structure, which must be truncated at some point. To achieve this, contributions from terms higher than the third order are neglected in both the equations themselves and during the construction of the moment equations. The resulting equations form a set of linear equations, from which the stability matrix is constructed. This matrix has a rather complex structure, making it solvable only numerically. For the numerical study of the system’s evolution, the method of variable directions was chosen. Due to the presence of a stochastic component in the analyzed system, the method was modified such that random fields with a specified distribution and correlation function, responsible for the noise contribution to the overall nonlinearity, are generated across entire layers. The developed methodology was tested on the reaction – diffusion model proposed by Barrio et al., according to the results of the study, they showed the similarity of the obtained structures with the pigmentation of fish. This paper focuses on the system behavior analysis in the neighborhood of a non-zero stationary point. The dependence of the real part of the eigenvalues on the wavenumber has been examined. In the linear analysis, a range of wavenumber values is identified in which Turing instability occurs. Nonlinear analysis and numerical simulation of the system’s evolution are conducted for model parameters that, in contrast, lie outside the Turing instability region. Nonlinear analysis found noise intensities of additive noise for which, despite the absence of conditions for the emergence of diffusion instability, the system transitions to an unstable state. The results of the numerical simulation of the evolution of the tested model demonstrate the process of forming spatial structures of Turing type under the influence of additive noise.

The article describes the migration process of a certain population, taking into account the spatial heterogeneity of the local capacity of the ecological niche. It is assumed that this spatial heterogeneity is caused by various natural or artificial factors. The mathematical model of the migration process under consideration is a Cauchy problem on a straight line for some quasi-linear partial differential equation of the first order, which is satisfied by the linear population density under consideration. In this paper, a general solution to this Cauchy problem is found for an arbitrary dependence of the local capacity of an ecological niche on the spatial coordinate. This general solution was applied to describe the migration of the population in question in two different cases: in the case of a dependence of the local capacity of the ecological niche on the spatial coordinate in the form of a smooth step and in the case of a hill-like dependence of the local capacity of the ecological niche on the spatial coordinate. In both cases, the solution to the Cauchy problem is expressed in terms of higher transcendental functions. By applying special relations to the model parameters, these higher transcendental functions are reduced to elementary functions, which makes it possible to obtain exact model solutions explicitly expressed in terms of elementary functions. With the help of these precise solutions, an extensive program of computational experiments has been implemented, showing how the initial population density of the Gaussian form is dispersed by the considered two types of spatial heterogeneity of the local capacity of the ecological niche. These computational experiments have shown that when passing through both step-like and hill-like spatial inhomogeneities of the local capacity of an ecological niche with a narrow Gaussian width of its initial density compared to the characteristic spatial scale of these inhomogeneities, the system forgets its initial state. In particular, if we interpret the system under study as a population living in an extended calm rectilinear river along its bed, then it can be argued that under this initial condition, after the current of this river carries the population under consideration through the area of spatial heterogeneity of the local capacity of the ecological niche, the population density becomes a quasi-rectangular function.

This paper considers the inverse problem of seismic exploration — determining the structure of the media based on the recorded wave response from it. Large cracks are considered as target objects, whose size and position are to be determined.

he direct problem is solved using the grid-characteristic method. The method allows using physically based algorithms for calculating outer boundaries of the region and contact boundaries inside the region. The crack is assumed to be thin, a special condition on the crack borders is used to describe the crack.

The inverse problem is solved using convolutional neural networks. The input data of the neural network are seismograms interpreted as images. The output data are masks describing the medium on a structured grid. Each element of such a grid belongs to one of two classes — either an element of a continuous geological massif, or an element through which a crack passes. This approach allows us to consider a medium with an unknown number of cracks.

The neural network is trained using only samples with one crack. The final testing of the trained network is performed using additional samples with several cracks. These samples are not involved in the training process. The purpose of testing under such conditions is to verify that the trained network has sufficient generality, recognizes signs of a crack in the signal, and does not suffer from overtraining on samples with a single crack in the media.

The paper shows that a convolutional network trained on samples with a single crack can be used to process data with multiple cracks. The networks detects fairly small cracks at great depths if they are sufficiently spatially separated from each other. In this case their wave responses are clearly distinguishable on the seismogram and can be interpreted by the neural network. If the cracks are close to each other, artifacts and interpretation errors may occur. This is due to the fact that on the seismogram the wave responses of close cracks merge. This cause the network to interpret several cracks located nearby as one. It should be noted that a similar error would most likely be made by a human during manual interpretation of the data. The paper provides examples of some such artifacts, distortions and recognition errors.

This paper examines the impact of the spatial resolution of a discretized (lattice) representation of the environment on the efficiency and correctness of optimal pathfinding in complex environments. Scenarios are considered that may include bottlenecks, non-uniform obstacle distributions, and areas of increased safety requirements in the immediate vicinity of obstacles. Despite the widespread use of lattice representations of the environment in robotics due to their compatibility with sensor data and support for classical trajectory planning algorithms, the resolution of these lattices has a significant impact on both goal reachability and optimal path performance. An algorithm is proposed that combines environmental connectivity analysis, trajectory optimization, and geometric safety refinement. In the first stage, the Leath algorithm is used to estimate the reachability of the target point by identifying a connected component containing the starting position. Upon confirmation of the target point’s reachability, the A* algorithm is applied to the nodes of this component in the second stage to construct a path that simultaneously minimizes both the path length and the risk of collision. In the third stage, a refined obstacle distance estimate is performed for nodes located in safety zones using a combination of the Gilbert – Johnson –Keerthi (GJK) and expanding polyhedron (EPA) algorithms. Experimental analysis revealed a nonlinear relationship between the probability of the existence and effectiveness of an optimal path and the lattice parameters. Specifically, reducing the spatial resolution of the lattice increases the likelihood of connectivity loss and target unreachability, while increasing its spatial resolution increases computational complexity without a proportional improvement in the optimal path’s performance.

The paper presents a cascade model of a physically supported neural network designed to predict pressure drop in three-phase flow (oil, gas, water) in a pipe section with various angles of inclination. To overcome the constraints of existing empirical correlations and computation-intensive numerical modeling methods, we propose an architecture that decomposes the problem into three sequential physically interpretable subtasks: regression prediction of the fluid hold-up coefficient, fluid flow regime classification, and pressure gradient evaluation. Each subtask is solved by a separate fully connected neural network, the output of which is passed to the next model in the cascade. Training and testing of the proposed architecture was performed on an extensive synthetic dataset (8 · 10⁷ records) generated using a semi-empirical model. Verification is performed on independent experimental data. A comparative analysis with a single fully connected (non-cascade) neural network is made, and the sensitivity of the models is examined using Sobol and Borgonovo methods. The cascade model demonstrates superior accuracy and ensures high interpretability of results by providing intermediate physical parameters (fluid hold-up coefficient, flow regime). The developed model has low computational complexity, which allows it to be used in real-time systems and digital twins of hydraulic systems in the oil and gas industry.

Aortic stenosis and other forms of aortic valve dysfunction are associated with impaired intracardiac hemodynamics, left ventricular overload, and an increased risk of cardiovascular complications. Assessment of valve function requires not only integral clinical indicators but also local mechanical and hemodynamic characteristics, which, as a rule, cannot be measured directly in vivo. Therefore, mathematical modeling is regarded as one of the main tools for the quantitative analysis of the aortic valve. Despite the widespread use of various deformable-solid models and coupled fluid-structure interaction formulations, FSI, for describing leaflet dynamics, the limits of applicability of simplified formulations relative to the fully coupled problem remain insufficiently defined. In this study, an idealized model of the aortic root with the sinuses of Valsalva and a tricuspid valve was considered. The leaflets were described using an anisotropic hyperelastic material model. Five computational scenarios were compared, including a fully coupled FSI formulation that accounts for both solid and fluid dynamics, as well as a deformable-solid model with four loading variants replacing the effect of blood flow, differing in the way pressure was represented and in the direction of load application to the leaflets. The comparison criteria included deformation, displacement, von Mises stress, leaflet oscillatory dynamics, and the geometric opening area of the valve. It was shown that the FSI model provides the most consistent description of valve function, including asymmetric leaflet opening, smoother opening dynamics, and the absence of pronounced nonphysiological flutter. Structural formulations with loads applied along the local normal to the leaflet surface lead to overestimation of deformation and stress, as well as to more pronounced oscillatory regimes. Scenarios with restricted load direction produce a more moderate response, but they also fail to reproduce the spatial load structure and the temporal organization of leaflet opening. It was concluded that, in aortic valve modeling, not only the magnitude of the pressure difference but also the way it is applied to the leaflets in space and time is of decisive importance. Structural deformable-solid models may be used for the qualitative assessment of selected mechanical trends, but they cannot serve as a full substitute for the FSI formulation in the analysis of leaflet kinematics, oscillatory regimes, stress-strain state, and valve opening dynamics.

A possible conformational change, which accompanies electron tranport in Rb. sphaeroides photosynthetic reaction center (RC), was studied using quantum-chemical approach. A kinetic model which takes into account two conformational states of RC is proposed. The model quantitatively describes experimental temperature dependencies of recombination reaction rate P⁺Q_A^- → PQ_A. Quantum-chemical modeling of primary quinone (Q_A) binding site permits one to propose a minor shift of Q_A as a conformational change of interest. The shift is accompanied by break of a hydrogen bond between 4–C=O group of Q_A and histidine M²¹⁹, and formation of a new hydrogen bond between Q_A and hydroxyl group of threonine M²²². Characteristics of this conformational change were obtained from quantum-chemical calculations and match parameters of kinetic model in qualitative fashion.

Mechanical unfolding of two identical in structure but differ in their amino acid sequences immunoglobulinbinding domains of proteins L and G under the action of external forces have been investigating  using the method of molecular dynamics with explicit model of solvent. Mechanical characteristics of these proteins have been calculated. It has been shown that in the way of the mechanical unfolding of both proteins appear intermediate states. Calculations revealed three significantly different ways of mechanical unfolding of proteins L and G.