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Gradient-free optimization methods or zeroth-order methods are widely used in training neural networks, reinforcement learning, as well as in industrial tasks where only the values of a function at a point are available (working with non-analytical functions). In particular, the method of error back propagation in PyTorch works exactly on this principle. There is a well-known fact that computer calculations use heuristics of floating-point numbers, and because of this, the problem of finiteness of the mantissa arises.

In this paper, firstly, we reviewed the most popular methods of gradient approximation: Finite forward/central difference (FFD/FCD), Forward/Central wise component (FWC/CWC), Forward/Central randomization on $l_2$ sphere (FSSG2/CFFG2); secondly, we described current theoretical representations of the noise introduced by the inaccuracy of calculating the function at a point: adversarial noise, random noise; thirdly, we conducted a series of experiments on frequently encountered classes of problems, such as quadratic problem, logistic regression, SVM, to try to determine whether the real nature of machine noise corresponds to the existing theory. It turned out that in reality (at least for those classes of problems that were considered in this paper), machine noise turned out to be something between adversarial noise and random, and therefore the current theory about the influence of the mantissa limb on the search for the optimum in gradient-free optimization problems requires some adjustment.

Use of algorithms based on neural networks can be inefficient for small amounts of experimental data. Authors consider a solution of this problem in the context of modelling of properties of ceramic composite materials reinforced with carbon nanotubes using perceptron complex. This approach allowed us to obtain a mathematical description of the object of study with a minimal amount of input data (the amount of necessary experimental samples decreased 2–3.3 times). Authors considered different versions of perceptron complex structures. They found that the most appropriate structure has perceptron complex with breakthrough of two input variables. The relative error was only 6%. The selected perceptron complex was shown to be effective for predicting the properties of ceramic composites. The relative errors for output components were 0.3%, 4.2%, 0.4%, 2.9%, and 11.8%.

A variant of import into ANSYS Mechanical APDL system of the model of behavior of flexible woven composite materials with reinforcing weaving cloth of linen at static stretching along the reinforcement yarns is offered. The import was carried out using an integration module based on the use of an analytical model of deformation of the material under study. The model is presented in the articles published earlier and takes into account the changes in the geometric structure occurring in the reinforcing layer of the material during the deformation process, the formation of irreversible deformations and the interaction of cross-lying reinforcing fabric threads. In the introduction input characteristics of the plain weave of the reinforcing fabric and the analytical model imported into ANSYS are briefly described. The input parameters of the module are the mechanical characteristics of the materials that make up the composite (binder and material of reinforcement yarns), the geometric characteristics of the interlacing of the reinforcing fabric. The algorithm for importing the model is based on the calculation and transfer in ANSYS of the calculated points of the material stress-strain diagram for uniaxial stretching along the reinforcement direction and using the Multilinear Kinematich Hardening model material embedded in the ANSYS. The analytical model imported with the help of the presented module allows to model a composite material with reinforcing fabric without a detailed description of the geometry of the interlacing of threads during modeling of the material as a whole. The imported model was verified. For verification full-scale experimental studies and numerical simulation of the stretching of samples from flexible woven composites were carried out. The analysis of the obtained results showed good qualitative and quantitative agreement of calculations.

Flexible woven composites are classified as high-tech innovative materials. Due to the combination of various components of the filler and reinforcement elements, such materials are used in construction, in the defense industry, in shipbuilding and aircraft construction, etc. In the domestic literature, insufficient attention is paid to woven composites that change their geometric structure of the reinforcing layer during deformation. This paper presents an analysis of the previously proposed complex approach to modeling the behavior of flexible woven composites under static uniaxial tension for further generalization of the approach to biaxial tension. The work is aimed at qualitative and quantitative description of mechanical deformation processes occurring in the structure of the studied materials under tension, which include straightening the strands of the reinforcing layer and increasing the value of mutual pressure of the cross-lying reinforcement strands. At the beginning of the deformation process, the straightening of the threads and the increase in mutual pressure of the threads are most intense. With the increase in the level of load, the change of these parameters slows down. For example, the bending of the reinforcement strands goes into the Central tension, and the value of the load from the mutual pressure is no longer increased (tends to constant). To simulate the described processes, the basic geometrical and mechanical parameters of the material affecting the process of forming are introduced, the necessary terminology and description of the characteristics are given. Due to the high geometric nonlinearity of the all processes described in the increments, as in the initial load values there is a significant deformation of the reinforcing layer. For the quantitative and qualitative description of mechanical deformation processes occurring in the reinforcing layer, analytical dependences are derived to determine the increment of the angle of straightening of reinforcement filaments and the load caused by the mutual pressure of the cross-lying filaments at each step of the load increment. For testing of obtained dependencies shows an example of their application for flexible woven composites brands VP4126, VP6131 and VP6545. The simulation results confirmed the assumptions about the processes of straightening the threads and slowing the increase in mutual pressure of the threads. The results and dependences presented in this paper are directly related to the further generalization of the previously proposed analytical models for biaxial tension, since stretching in two directions will significantly reduce the straightening of the threads and increase the amount of mutual pressure under similar loads.

The paper is devoted to convex-concave saddle point problems where the objective is a sum of a large number of functions. Such problems attract considerable attention of the mathematical community due to the variety of applications in machine learning, including adversarial learning, adversarial attacks and robust reinforcement learning, to name a few. The individual functions in the sum usually represent losses related to examples from a data set. Additionally, the formulation admits a possibly nonsmooth composite term. Such terms often reflect regularization in machine learning problems. We assume that the dimension of one of the variable groups is relatively small (about a hundred or less), and the other one is large. This case arises, for example, when one considers the dual formulation for a minimization problem with a moderate number of constraints. The proposed approach is based on using Vaidya’s cutting plane method to minimize with respect to the outer block of variables. This optimization algorithm is especially effective when the dimension of the problem is not very large. An inexact oracle for Vaidya’s method is calculated via an approximate solution of the inner maximization problem, which is solved by the accelerated variance reduced algorithm Katyusha. Thus, we leverage the structure of the problem to achieve fast convergence. Separate complexity bounds for gradients of different components with respect to different variables are obtained in the study. The proposed approach is imposing very mild assumptions about the objective. In particular, neither strong convexity nor smoothness is required with respect to the low-dimensional variable group. The number of steps of the proposed algorithm as well as the arithmetic complexity of each step explicitly depend on the dimensionality of the outer variable, hence the assumption that it is relatively small.

This article proposes the sumo-atclib framework, which provides a convenient uniform interface for testing adaptive control algorithms with different limitations, for example, restrictions on phase durations, phase sequences, restrictions on the minimum time between control actions, which uses the open source microscopic transport modeling environment SUMO. The framework shares the functionality of controllers (class TrafficController) and a monitoring and detection system (class StateObserver), which repeats the architecture of real traffic light objects and adaptive control systems and simplifies the testing of new algorithms, since combinations of different controllers and vehicle detection systems can be freely varied. Also, unlike most existing solutions, the road class Road has been added, which combines a set of lanes, this allows, for example, to determine the adjacency of regulated intersections, in cases when the number of lanes changes on the way from one intersection to another, and therefore the road graph is divided into several edges. At the same time, the algorithms themselves use the same interface and are abstracted from the specific parameters of the detectors, network topologies, that is, it is assumed that this solution will allow the transport engineer to test ready-made algorithms for a new scenario, without the need to adapt them to new conditions, which speeds up the development process of the control system, and reduces design overhead. At the moment, the package contains examples of MaxPressure algorithms and the Q-learning reinforcement learning method, the database of examples is also being updated. The framework also includes a set of SUMO scripts for testing algorithms, which includes both synthetic maps and well-verified SUMO scripts such as Cologne and Ingolstadt. In addition, the framework provides a set of automatically calculated metrics, such as total travel time, delay time, average speed; the framework also provides a ready-made example for visualization of metrics.

The method of modelling of a compact bone tissue microstructure is presented. The modelling sample is considered as set of the structural elements containing reinforcing element – osteon and a matrix. The form of structural elements is defined by distances to next osteons and directions of next osteons arrangement. Calculation of the stress and strain state of the modelling sample is carried out at tension in program complex ANSYS. Results of calculation have shown, that haversian canals are stress concentrators.

This article discusses the mechanical properties of carbon nanotube (CNT)-reinforced platinum under uniaxial tensile loading using the molecular dynamics method. A review of current computational and experimental studies on the use of carbon nanotube-reinforced composites from a structural point of view. However, quantitative and qualitative studies of CNTs to improve the properties of composites are still rare. Composite selection is a promising application for platinum alloys in many cases where they may be subjected to mechanical stress, including in biocompatibility sources. Pt-reinforced with CNTs may have additional possibilities for implantation of the implant and at the same time obtain the required mechanical characteristics.

The structure of the composite is composed of a Pt crystal with a face-centered cubic lattice with a constant of 3.92 Å and a carbon nanotube. The Pt matrix has the shape of a cube with dimensions of $43.1541 Å \times 43.1541 Å \times 43.1541 Å$. The hole size in the average platinum dimension is the radius of the carbon nanotube of the «zigzag» type (8,0), which is 2.6 Å. A carbon nanotube is placed in a hole with a radius of 4.2 Å. At such parameters, the maximum energy level was mutually observed. The model under consideration is contained in 320 atomic bombs and 5181 atomic platinum. The volume fraction of deaths in the Pt-C composite is 5.8%. At the first stage of the study, the strain rate was analyzed for stress-strain and energy change during uniaxial action on the Pt-C composite.

Analysis of the strain rate study showed that the consumption yield strength increases with high strain rate, and the elasticity has increased density with decreasing strain rate. This work also increased by 40% for Pt-C, the elasticity of the composite decreased by 42.3%. In general, fracture processes are considered in detail, including plastic deformation on an atomistic scale.

The study of the structural characteristics of composites and nanostructures is of fundamental importance in materials science. Theoretical and numerical modeling and simulation of the mechanical properties of nanostructures is the main tool that allows for complex studies that are difficult to conduct only experimentally. One example of nanostructures considered in this work are carbon nanotubes (CNTs), which have good thermal and electrical properties, as well as low density and high Young’s modulus, making them the most suitable reinforcement element for composites, for potential applications in aerospace, automotive, metallurgical and biomedical industries. In this review, we reviewed the modeling methods, mechanical properties, and applications of CNT-reinforced metal matrix composites. Some modeling methods applicable in the study of composites with polymer and metal matrices are also considered. Methods such as the gradient descent method, the Monte Carlo method, methods of molecular statics and molecular dynamics are considered. Molecular dynamics simulations have been shown to be excellent for creating various composite material systems and studying the properties of metal matrix composites reinforced with carbon nanomaterials under various conditions. This paper briefly presents the most commonly used potentials that describe the interactions of composite modeling systems. The correct choice of interaction potentials between parts of composites directly affects the description of the phenomenon being studied. The dependence of the mechanical properties of composites on the volume fraction of the diameter, orientation, and number of CNTs is detailed and discussed. It has been shown that the volume fraction of carbon nanotubes has a significant effect on the tensile strength and Young’s modulus. The CNT diameter has a greater impact on the tensile strength than on the elastic modulus. An example of works is also given in which the effect of CNT length on the mechanical properties of composites is studied. In conclusion, we offer perspectives on the direction of development of molecular dynamics modeling in relation to metal matrix composites reinforced with carbon nanomaterials.