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The work is devoted to the study of the influence of nonlinear processes in the boundary layer on the general nature of gas flows in microchannels of technical systems. Such a study is actually concerned with nanotechnology problems. One of the important problems in this area is the analysis of gas flows in microchannels in the case of transient and supersonic flows. The results of this analysis are important for the gas-dynamic spraying techique and for the synthesis of new nanomaterials. Due to the complexity of the implementation of full-scale experiments on micro- and nanoscale, they are most often replaced by computer simulations. The efficiency of computer simulations is achieved by both the use of new multiscale models and the combination of mesh and particle methods. In this work, we use the molecular dynamics method. It is applied to study the establishment of a gas microflow in a metal channel. Nitrogen was chosen as the gaseous medium. The metal walls of the microchannels consisted of nickel atoms. In numerical experiments, the accommodation coefficients were calculated at the boundary between the gas flow and the metal wall. The study of the microsystem in the boundary layer made it possible to form a multicomponent macroscopic model of the boundary conditions. This model was integrated into the macroscopic description of the flow based on a system of quasi-gas-dynamic equations. On the basis of such a transformed gas-dynamic model, calculations of microflow in real microsystem were carried out. The results were compared with the classical calculation of the flow, which does not take into account nonlinear processes in the boundary layer. The comparison showed the need to use the developed model of boundary conditions and its integration with the classical gas-dynamic approach.

The development of a viral infection in the organism is a complex process which depends on the competition race between virus replication in the host cells and the immune response. To study different regimes of infection progression, we analyze the general mathematical model of immune response to viral infection. The model consists of two ODEs for virus and immune cells non-dimensionalized concentrations. The proliferation rate of immune cells in the model is represented by a bell-shaped function of the virus concentration. This function increases for small virus concentrations describing the antigen-stimulated clonal expansion of immune cells, and decreases for sufficiently high virus concentrations describing down-regulation of immune cells proliferation by the infection. Depending on the virus virulence, strength of the immune response, and the initial viral load, the model predicts several scenarios: (a) infection can be completely eliminated, (b) it can remain at a low level while the concentration of immune cells is high; (c) immune cells can be essentially exhausted, or (d) completely exhausted, which is accompanied (c, d) by high virus concentration. The analysis of the model shows that virus concentration can oscillate as it gradually converges to its equilibrium value. We show that the considered model can be obtained by the reduction of a more general model with an additional equation for the total viral load provided that this equation is fast. In the case of slow kinetics of the total viral load, this more general model should be used.

This article reviews both historical achievements and modern results in the field of Markov Decision Process (MDP) and convex optimization. This review is the first attempt to cover the field of reinforcement learning in Russian in the context of convex optimization. The fundamental Bellman equation and the criteria of optimality of policy — strategies based on it, which make decisions based on the known state of the environment at the moment, are considered. The main iterative algorithms of policy optimization based on the solution of the Bellman equations are also considered. An important section of this article was the consideration of an alternative to the $Q$-learning approach — the method of direct maximization of the agent’s average reward for the chosen strategy from interaction with the environment. Thus, the solution of this convex optimization problem can be represented as a linear programming problem. The paper demonstrates how the convex optimization apparatus is used to solve the problem of Reinforcement Learning (RL). In particular, it is shown how the concept of strong duality allows us to naturally modify the formulation of the RL problem, showing the equivalence between maximizing the agent’s reward and finding his optimal strategy. The paper also discusses the complexity of MDP optimization with respect to the number of state–action–reward triples obtained as a result of interaction with the environment. The optimal limits of the MDP solution complexity are presented in the case of an ergodic process with an infinite horizon, as well as in the case of a non-stationary process with a finite horizon, which can be restarted several times in a row or immediately run in parallel in several threads. The review also reviews the latest results on reducing the gap between the lower and upper estimates of the complexity of MDP optimization with average remuneration (Averaged MDP, AMDP). In conclusion, the real-valued parametrization of agent policy and a class of gradient optimization methods through maximizing the $Q$-function of value are considered. In particular, a special class of MDPs with restrictions on the value of policy (Constrained Markov Decision Process, CMDP) is presented, for which a general direct-dual approach to optimization with strong duality is proposed.

This paper provides an overview of studies on nonlinear supratransmission and related phenomena. This effect consists in the transfer of energy at frequencies not supported by the systems under consideration. The supratransmission does not depend on the integrability of the system, it is resistant to damping and various classes of boundary conditions. In addition, a nonlinear discrete medium, under certain general conditions imposed on the structure, can create instability due to external periodic influence. This instability is the generative process underlying the nonlinear supratransmission. This is possible when the system supports nonlinear modes of various nature, in particular, discrete breathers. Then the energy penetrates into the system as soon as the amplitude of the external harmonic excitation exceeds the maximum amplitude of the static breather of the same frequency.

The effect of nonlinear supratransmission is an important property of many discrete structures. A necessary condition for its existence is the discreteness and nonlinearity of the medium. Its manifestation in systems of various nature speaks of its fundamentality and significance. This review considers the main works that touch upon the issue of nonlinear supratransmission in various systems, mainly model ones.

Many teams of authors are studying this effect. First of all, these are models described by discrete equations, including sin-Gordon and the discrete Schr¨odinger equation. At the same time, the effect is not exclusively model and manifests itself in full-scale experiments in electrical circuits, in nonlinear chains of oscillators, as well as in metastable modular metastructures. There is a gradual complication of models, which leads to a deeper understanding of the phenomenon of supratransmission, and the transition to disordered structures and those with elements of chaos structures allows us to talk about a more subtle manifestation of this effect. Numerical asymptotic approaches make it possible to study nonlinear supratransmission in complex nonintegrable systems. The complication of all kinds of oscillators, both physical and electrical, is relevant for various real devices based on such systems, in particular, in the field of nano-objects and energy transport in them through the considered effect. Such systems include molecular and crystalline clusters and nanodevices. In the conclusion of the paper, the main trends in the research of nonlinear supratransmission are given.

The study of the properties of seismic noise in Kamchatka is based on the idea that noise is an important source of information about the processes preceding strong earthquakes. The hypothesis is considered that an increase in seismic hazard is accompanied by a simplification of the statistical structure of seismic noise and an increase in spatial correlations of its properties. The entropy of the distribution of squared wavelet coefficients, the width of the carrier of the multifractal singularity spectrum, and the Donoho – Johnstone index were used as statistics characterizing noise. The values of these parameters reflect the complexity: if a random signal is close in its properties to white noise, then the entropy is maximum, and the other two parameters are minimum. The statistics used are calculated for 6 station clusters. For each station cluster, daily median noise properties are calculated in successive 1-day time windows, resulting in an 18-dimensional (3 properties and 6 station clusters) time series of properties. To highlight the general properties of changes in noise parameters, a principal component method is used, which is applied for each cluster of stations, as a result of which the information is compressed into a 6-dimensional daily time series of principal components. Spatial noise coherences are estimated as a set of maximum pairwise quadratic coherence spectra between the principal components of station clusters in a sliding time window of 365 days. By calculating histograms of the distribution of cluster numbers in which the minimum and maximum values of noise statistics are achieved in a sliding time window of 365 days in length, the migration of seismic hazard areas was assessed in comparison with strong earthquakes with a magnitude of at least 7.

Deep learning’s power stems from complex architectures; however, these can lead to overfitting, where models memorize training data and fail to generalize to unseen examples. This paper proposes a novel probabilistic approach to mitigate this issue. We introduce two key elements: Truncated Log-Uniform Prior and Truncated Log-Normal Variational Approximation, and Automatic Relevance Determination (ARD) with Bayesian Deep Neural Networks (BDNNs). Within the probabilistic framework, we employ a specially designed truncated log-uniform prior for noise. This prior acts as a regularizer, guiding the learning process towards simpler solutions and reducing overfitting. Additionally, a truncated log-normal variational approximation is used for efficient handling of the complex probability distributions inherent in deep learning models. ARD automatically identifies and removes irrelevant features or weights within a model. By integrating ARD with BDNNs, where weights have a probability distribution, we achieve a variational bound similar to the popular variational dropout technique. Dropout randomly drops neurons during training, encouraging the model not to rely heavily on any single feature. Our approach with ARD achieves similar benefits without the randomness of dropout, potentially leading to more stable training.

To evaluate our approach, we have tested the model on two datasets: the Canadian Institute For Advanced Research (CIFAR-10) for image classification and a dataset of Macroscopic Images of Wood, which is compiled from multiple macroscopic images of wood datasets. Our method is applied to established architectures like Visual Geometry Group (VGG) and Residual Network (ResNet). The results demonstrate significant improvements. The model reduced overfitting while maintaining, or even improving, the accuracy of the network’s predictions on classification tasks. This validates the effectiveness of our approach in enhancing the performance and generalization capabilities of deep learning models.

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.

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.

The paper presents an optimization approach to microstructure simulation. Porosity function was optimized by numerical method, grain-size model was optimized by complex method based on criteria of model quality. Methods have been validated on examples. Presented new regression model of model quality. Actual application of proposed method is 3D reconstruction of core sample microstructure. Presented results suggest to prolongation of investigations.