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In the last decades, the development of methods for increasing the efficiency of hydrocarbon extraction in fields with unconventional reserves containing large amounts of gas condensate is of great importance. This makes important the development of methods of mathematical modeling that realistically describe physical processes in a gas-condensate mixture in a porous medium.

In the paper, a mathematical model which describes the dynamics of the pressure, velocity and concentration of the components of a two-component two-phase mixture entering a laboratory model of plast filled with a porous substance with known physicochemical properties is considered. The mathematical model is based on a system of nonlinear spatially one-dimensional partial differential equations with the corresponding initial and boundary conditions. Laboratory experiments show that during a finite time the system stabilizes, what gives a basis to proceed to the stationary formulation of the problem.

The numerical solution of the formulated system of ordinary differential equations is realized in the Maple environment on the basis of the Runge–Kutta procedure. It is shown that the physical parameters of the gascondensate mixture, which characterize the modeled system in the stabilization regime, obtained on this basis, are in good agreement with the available experimental data. This confirms the correctness of the chosen approach and the validity of its further application and development for computer modeling of physical processes in gas-condensate mixtures in a porous medium. The paper presents a mathematical formulation of the system of partial differential equations and of respective system stationary equations, describes the numerical approach, and discusses the numerical results obtained in comparison with experimental data.

In this paper, we introduce a new version of Gauss–Newton method for solving a system of nonlinear equations based on ideas of the residual upper bound for a system of nonlinear equations and a quadratic regularization term. The introduced Gauss–Newton method in practice virtually forms the whole parameterized family of the methods solving systems of nonlinear equations and regression problems. The developed family of Gauss–Newton methods completely consists of iterative methods with generalization for cases of non-euclidean normed spaces, including special forms of Levenberg–Marquardt algorithms. The developed methods use the local model based on a parameterized proximal mapping allowing us to use an inexact oracle of «black–box» form with restrictions for the computational precision and computational complexity. We perform an efficiency analysis including global and local convergence for the developed family of methods with an arbitrary oracle in terms of iteration complexity, precision and complexity of both local model and oracle, problem dimensionality. We present global sublinear convergence rates for methods of the proposed family for solving a system of nonlinear equations, consisting of Lipschitz smooth functions. We prove local superlinear convergence under extra natural non-degeneracy assumptions for system of nonlinear functions. We prove both local and global linear convergence for a system of nonlinear equations under Polyak–Lojasiewicz condition for proposed Gauss– Newton methods. Besides theoretical justifications of methods we also consider practical implementation issues. In particular, for conducted experiments we present effective computational schemes for the exact oracle regarding to the dimensionality of a problem. The proposed family of methods unites several existing and frequent in practice Gauss–Newton method modifications, allowing us to construct a flexible and convenient method implementable using standard convex optimization and computational linear algebra techniques.

A model of combustion of premixed mixture of gases with one global chemical reaction is considered, the model includes equations of the second order for temperature of mixture and concentrations of fuel and oxidizer, and the right-hand sides of these equations contain the reaction rate function. This function depends on five unknown parameters of the global reaction and serves as approximation to multistep reaction mechanism. The model is reduced, after replacement of variables, to one equation of the second order for temperature of mixture that transforms to a first-order equation for temperature derivative depending on temperature that contains a parameter of flame propagation velocity. Thus, for computing the parameter of burning velocity, one has to solve Dirichlet problem for first-order equation, and after that a model dependence of burning velocity on mixture equivalence ratio at specified reaction rate parameters will be obtained. Given the experimental data of dependence of burning velocity on mixture equivalence ratio, the problem of optimal selection of reaction rate parameters is stated, based on minimization of the mean square deviation of model values of burning velocity on experimental ones. The aim of our study is analysis of uniqueness of this problem solution. To this end, we apply computational experiment during which the problem of global search of optima is solved using multistart of gradient descent. The computational experiment clarifies that the inverse problem in this statement is underdetermined, and every time, when running gradient descent from a selected starting point, it converges to a new limit point. The structure of the set of limit points in the five-dimensional space is analyzed, and it is shown that this set can be described with three linear equations. Therefore, it might be incorrect to tabulate all five parameters of reaction rate based on just one match criterion between model and experimental data of flame propagation velocity. The conclusion of our study is that in order to tabulate reaction rate parameters correctly, it is necessary to specify the values of two of them, based on additional optimality criteria.

While working on activity recognition using wearable sensors for healthcare applications, the main issue arises in the classification of activities. When we attempt to classify activities like walking, sitting, or running from accelerometer and gyroscope data, the signals often overlap and noise complicates the classification process. The existing methods do not have solid mathematical foundations to handle this issue. We started with the standard magnitude approach where one can compute $m =  \sqrt{a^2_1 + a^2_2 + a^2_3}$ from the accelerometer readings, but this approach failed because different activities ended up in overlapping regions. We therefore developed a different approach. Instead of collapsing the 6-dimensional sensor data into simple magnitudes, we keep all six dimensions and treat each activity as a rectangular box in this 6D space. We define these boxes using simple interval constraints. For example, walking occurs when the $x$-axis accelerometer reading is between $2$ and $4$, the $y$-axis reading is between $9$ and $10$, and so on. The key breakthrough is what we call a separability index $s = \frac{d_{\min}^{}}{\sigma}$ that determines how accurately the classification will work. Here dmin represents how far apart the activity boxes are, and $\sigma$ represents the amount of noise present. From this simple idea, we derive a mathematical formula $P(\text{error}) \leqslant (n-1)\exp\left(-\frac{s^2}8\right)$  that predicts the error rate even before initiating the experiment. We tested this on the standard UCI-HAR and WISDM datasets and achieved $86.1 %$ accuracy. The theoretical predictions matched the actual results within $3 %$. This approach outperforms the traditional magnitude methods by $30.6 %$ and explains why certain activities overlap with each other.

Monte Carlo (MC) reinforcement learning suffers from high sample complexity, especially in environments with sparse rewards, large state spaces, and strongly correlated trajectories that reduce the statistical efficiency of return estimation. These well-known limitations often lead to slow convergence and unstable learning dynamics, particularly in settings where only a small fraction of collected trajectories is actually informative for policy improvement. A key challenge is therefore to identify a compact yet diverse subset of episodes that contributes most to the accuracy of value estimates while preserving sufficient exploration of the environment. To address this challenge, we reformulate episode selection as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using quantum-inspired sampling techniques. Our method, MC+ QUBO, inserts a combinatorial filtering step into the standard MC policy-evaluation pipeline: given a batch of trajectories, it selects a subset that maximizes cumulative reward and encourages broad state-space coverage. This selection procedure is expressed as a QUBO model, where linear terms favor high-return episodes, quadratic terms penalize redundancy between trajectories, and additional coupling terms can be used to enforce coverage-related constraints or promote structural diversity. Within this framework, we investigate two black-box QUBO solvers: Simulated Quantum Annealing (SQA), which emulates tunneling-based exploration of the search landscape, and Simulated Bifurcation (SB), a dynamical-systems-based iterative optimization method. Both solvers demonstrate the ability to efficiently navigate the combinatorial structure of the trajectory-selection problem and to handle batch sizes that are otherwise computationally expensive for exhaustive or deterministic search. Experiments in a finite-horizon GridWorld environment show that MC+QUBO consistently outperforms vanilla MC in convergence speed, stability of return estimates, and final policy quality. These results highlight the promise of quantum-inspired optimization as a practical decision-making subroutine within reinforcement-learning algorithms, offering a scalable way to improve sample efficiency without modifying the underlying learning paradigm.

This paper describes a method for generating a synthetic database of stenoses, consisting of 1620 entries. Each entry represents the results of a numerical experiment simulating the three-dimensional flow of a viscous incompressible fluid through a tube with a variable cross-section: pressure drop, mean flow rate, cross-sectionally averaged inlet blood flow velocity, maximum stenosis severity, stenosis length, stenosis asymmetry, tube radius, and Reynolds number. The database was validated by comparison with other models (with elastic walls) and bench experiments, showing a deviation in pressure drops of no more than 4%. The synthetic stenosis database was used to train a physics-informed neural network for the rapid estimation of pressure drop based on four key input parameters: Reynolds number, stenosis length, stenosis severity, and stenosis asymmetry coefficient. The physics-informed aspect was achieved by introducing penalties into the loss function for the absence of a positive pressure drop and for the lack of monotonicity of the pressure drop with respect to the input parameters. The physics-informed neural network demonstrated higher accuracy on hemodynamically significant stenoses when tested on a validation set and on new stenoses not represented in the database. The mean relative error for stenoses with a length of 8 healthy vessel radii was 6% for the physics-informed network and 13% for a classical neural network. The errors for short stenoses with a length of 4 radii were nearly identical: 9.5% for the physics-informed network and 10% for the classical neural network. The developed method for the functional assessment of the hemodynamic significance of stenoses can be used both as a standalone tool for clinical stenosis evaluation and as a component of network blood flow models. The approach becomes most relevant when modeling multi-vessel disease, which is predominant in clinical practice. The key advantage of the method lies in the physical correctness of the results and accuracy comparable to classical modeling, but with significantly lower computational costs.

Main theoretical considerations of dynamical changes of forest vegetation are discussed. It is shown that vegetation dynamics (succession) and soil dynamics are linked, and common dynamics is a result of biological turnover of nutrition elements. Main modelling approaches are examined and unsolved problems are formulated. An example of computer experiment on comparison of forest growth at stationary and global warming scenario is considered.

Статья представляет собой пример компьютерного моделирования в области инженерной механики. Численным методом находятся поля скорости в гидроциклоне, которые недоступны прямому измерению. Рассматривается численное моделирование трехмерной гидродинамики на основе k-ε RNG модели турбулентности в гидроциклоне со встроенным инжектором, содержащим 5 тангенциально направленных сопла. Показано, что направление движения инжектируемой жидкости зависит от расхода жидкости через инжектор. Расчеты показывают в соответствии с экспериментами, что зависимость сплит-параметра от расхода инжектируемой жидкости имеет немонотонный характер, связанный с отношением мощности основного потока и инжектируемой жидкости.

Based on CFD computations we provide the analysis of the possibilities for using modern hybrid distributed computational environments for large complex system simulation. We argue that only multilevel approach supported by new mathematical models of transport properties, dynamical representation of the problem with transport and internal processes separated, and modern paradigm of programming, taking into account specific properties of heterogeneous system, will make it possible to scale the problem effectively.

The currently performed mathematical and computer modeling of thermal processes in technical systems is based on an assumption that all the parameters determining thermal processes are fully and unambiguously known and identified (i.e., determined). Meanwhile, experience has shown that parameters determining the thermal processes are of undefined interval-stochastic character, which in turn is responsible for the intervalstochastic nature of thermal processes in the electronic system. This means that the actual temperature values of each element in an technical system will be randomly distributed within their variation intervals. Therefore, the determinative approach to modeling of thermal processes that yields specific values of element temperatures does not allow one to adequately calculate temperature distribution in electronic systems. The interval-stochastic nature of the parameters determining the thermal processes depends on three groups of factors: (a) statistical technological variation of parameters of the elements when manufacturing and assembling the system; (b) the random nature of the factors caused by functioning of an technical system (fluctuations in current and voltage; power, temperatures, and flow rates of the cooling fluid and the medium inside the system); and (c) the randomness of ambient parameters (temperature, pressure, and flow rate). The interval-stochastic indeterminacy of the determinative factors in technical systems is irremediable; neglecting it causes errors when designing electronic systems. A method that allows modeling of unsteady interval-stochastic thermal processes in technical systems (including those upon interval indeterminacy of the determinative parameters) is developed in this paper. The method is based on obtaining and further solving equations for the unsteady statistical measures (mathematical expectations, variances and covariances) of the temperature distribution in an technical system at given variation intervals and the statistical measures of the determinative parameters. Application of the elaborated method to modeling of the interval-stochastic thermal process in a particular electronic system is considered.