General Nonlinear Problems Research Articles

Mathematical modeling of the process of nonlinear deformation of thin-walled structures

Objective. The objective is to develop a unified method for solving a general nonlinear boundary value problem associated with discontinuous phenomena, which allows identifying all the characteristic features of the behavior of thin-walled systems under load. The issues of nonlinear deformation, loss of stability of the initial equilibrium shape and post-critical behavior are considered using the example of a thin spherical shell. Method. The problem is solved by numerical and analytical methods, representing a set of methods of catastrophe theory and the finite difference method of increased accuracy. The main attention is paid to the mathematical aspects of the phenomena under consideration. Result. The parameters of the stressstrain state of subcritical, critical and postcritical deformation are determined using a spherical shell as an example. The relationships between the limit and bifurcation values of the load parameters are obtained, allowing us to determine the group of the limit state of the achieved level of the stress-strain state of the structure. Conclusion. The solution of the general problem allows us to obtain complete and necessary information to determine the degree of danger of the states of structures and ensure their reliability.

A Multilinear HJB-POD Method for the Optimal Control of PDEs on a Tree Structure

Optimal control problems driven by evolutionary partial differential equations arise in many industrial applications and their numerical solution is known to be a challenging problem. One approach to obtain an optimal feedback control is via the Dynamic Programming principle. Nevertheless, despite many theoretical results, this method has been applied only to very special cases since it suffers from the curse of dimensionality. Our goal is to mitigate this crucial obstruction developing a version of dynamic programming algorithms based on a tree structure and exploiting the compact representation of the dynamical systems based on tensors notations via a model reduction approach. Here, we want to show how this algorithm can be constructed for general nonlinear control problems and to illustrate its performances on a number of challenging numerical tests introducing novel pruning strategies that improve the efficacy of the method. Our numerical results indicate a large decrease in memory requirements, as well as computational time, for the proposed problems. Moreover, we prove the convergence of the algorithm and give some hints on its implementation.

Mainardi smoothing homotopy method for solving nonlinear optimal control problems

This paper proposes a new type of smoothing homotopy method for solving general nonlinear optimal control problems via the indirect method, leveraging the Mainardi kernel as the smoothing kernel. The Mainardi kernel is firstly derived from the fundamental solution of the time-fractional diffusion-wave equation, representing a generalized form of the Gaussian kernel. By altering the fractional derivative order, the kernel can seamlessly switch between non-Gaussian and Gaussian forms. Then, parts of the two-point boundary value problems are convolved with the smoothing kernel, and the resulting surrogates are incorporated into the necessary conditions, replacing the terminal state and costate variables. The homotopy process is used for the smoothing parameter: increasing this parameter enhances the smoothing effect, simplifying the homotopy problems, while a parameter value of zero implies no smoothing, reverting to the original problems. Additionally, explicit expressions for the Mainardi kernel at specific derivative orders are derived, avoiding the inefficiencies associated with fractional derivatives. Simulation examples demonstrate that the proposed method offers significant advantages in flexibility and convergence compared to the traditional Gaussian smoothing homotopy method.

Variational formulation and monolithic solution of computational homogenization methods

AbstractIn this contribution, we derive a consistent variational formulation for computational homogenization methods and show that traditional FE and IGA approaches are special discretization and solution techniques of this most general framework. This allows us to enhance dramatically the numerical analysis as well as the solution of the arising algebraic system. In particular, we expand the dimension of the continuous system, discretize the higher dimensional problem consistently and apply afterwards a discrete null‐space matrix to remove the additional dimensions. A benchmark problem, for which we can obtain an analytical solution, demonstrates the superiority of the chosen approach aiming to reduce the immense computational costs of traditional FE and IGA formulations to a fraction of the original requirements. Finally, we demonstrate a further reduction of the computational costs for the solution of general nonlinear problems.

Error estimates of the direct discontinuous Galerkin method for two-dimensional nonlinear convection–diffusion equations: Superconvergence analysis

This paper is concerned with superconvergence properties of the direct discontinuous Galerkin (DDG) method for two-dimensional nonlinear convection–diffusion equations. To copy with the complexity and difficulty caused by the nonlinear term, we utilize the method of Taylor expansion and correction idea to achieve our superconvergence goal. Specifically, we first adopt Taylor expansion to decompose the error into two parts: a high-order nonlinear term and a lower-order linear term. Then by using the idea of correction function for the linear term, a high order error bound is obtained. By doing so, a unified analysis of DDG method for general nonlinear problems is finally established. We prove that, for any piecewise tensor-product polynomials of degree k≥2, the DDG solution is superconvergent at nodes and Lobatto points, with an order of O(h2k) and O(hk+2), respectively. Moreover, superconvergence properties for the derivative approximation are also studied and the superconvergence points are identified at Gauss points, with an order of O(hk+1). Numerical experiments are presented to confirm the sharpness of all the theoretical findings.

Error analysis of Haar wavelet-based Galerkin numerical method with application to various nonlinear optimal control problems

ABSTRACT First, this paper defines a general nonlinear optimal control problem with state/control constraints and its approximation problem as the Haar wavelet Galerkin optimal control problem (HWGOCP). Then, a Haar wavelet-based Galerkin numerical method has been developed, which converts it to a nonlinear optimization problem. We theoretically prove that a Haar wavelet feasible solution of HWGOCP will exist. We also show that the approximate solutions of HWGOCP are consistent and converge to the optimal solution of the problem. A variety of application problems have been considered, which include optimal control of tumour growth using Chemotherapy drugs, optimal control of infection via the SIS model using treatment, the Brachistochrone problem in mechanics, optimal control of mold using a fungicide, optimal control of pH value of a chemical reaction to determine the quality of a product, etc.

A cooperative neural dynamic model for solving general convex nonlinear optimization problems with fuzzy parameters and an application in manufacturing systems

SummaryIn the presented study, the solution of the fuzzy nonlinear optimization problems (FNLOPs) is calculated using a recurrent neural network (RNN) model. Since there is a few research for solving FNLOP by RNN's, we give a new approach to solve the problem. By reducing the original program to an interval problem and then weighting problem, the Karush–Kuhn–Tucker (KKT) conditions are given. Moreover, we use the KKT conditions into a RNN as an important tool to solve the problem. Besides, the global convergence properties and the Lyapunov stability of the dynamic model are studied in this study. In the final step, some illustrative examples are considered to establish the obtained results. Reported results are compared with some others network models.

A non-intrusive bi-fidelity reduced basis method for time-independent problems

Scientific and engineering problems often involve parametric partial differential equations (PDEs), such as uncertainty quantification, optimizations, and inverse problems. However, solving these PDEs repeatedly can be prohibitively expensive, especially for large-scale complex applications. To address this issue, reduced order modeling (ROM) has emerged as an effective method to reduce computational costs. However, ROM often requires significant modifications to the existing code, which can be time-consuming and complex, particularly for large-scale legacy codes. Non-intrusive methods have gained attention as an alternative approach. However, most existing non-intrusive approaches are purely data-driven and may not respect the underlying physics laws during the online stage, resulting in less accurate approximations of the reduced solution. In this study, we propose a new non-intrusive bi-fidelity reduced basis method for time-independent parametric PDEs. Our algorithm utilizes the discrete operator, solutions, and right-hand sides obtained from the high-fidelity legacy solver. By leveraging a low-fidelity model, we efficiently construct the reduced operator and right-hand side for new parameter values during the online stage. Unlike other non-intrusive ROM methods, we enforce the reduced equation during the online stage. In addition, the non-intrusive nature of our algorithm makes it straightforward and applicable to general nonlinear time-independent problems. We demonstrate its performance through several benchmark examples, including nonlinear and multiscale PDEs.

Higher-order interior point methods for convex nonlinear programming

This paper extends the concept of higher-order search directions within interior point methods to convex nonlinear programming. This includes the mathematical framework needed to compute the higher-order derivatives. The paper also highlights some special cases where the computation of these higher-order derivatives is simplified and a dimensional lifting procedure for transforming a large number of general nonlinear problems into one of these more efficient forms. The paper further describes the algorithmic development required to employ these higher-order search directions in a practical algorithm. Computational results are presented for a large number of test problems, highlighting higher-order methods’ strong potential for decreasing iteration count and their case-by-case potential for decreasing CPU time.

Numerical analysis of a space-time continuous Galerkin method for the nonlinear Sobolev equation

In this work, we shall use space-time continuous Galerkin (STCG) method to analyze the nonlinear Sobolev equation. This method is different from the conventional finite element methods, that is, it not only uses finite element to discrete spatial variables but also uses finite element to discrete temporal variable. Therefore it is easier to obtain high order accuracy in time and space than the conventional finite element methods and has the good stability. Also, it does not need time step size and space mesh parameter to satisfy the stability conditions. In this paper, we show a detailed analysis including the proof of the existence and uniqueness of the numerical solution and the a priori error estimate. Especially, the analysis method given here can be similarly applied to the more general nonlinear problems. At last, the numerical examples reveal that the iterative method is more efficient than the linearized method for the nonlinear problems with STCG method. Also, the numerical experiments show that the STCG method is more efficient than the space-time discontinuous Galerkin (STDG) method in the practical calculations.

A Python Implementation of a Robust Multi-Harmonic Balance With Numerical Continuation and Automatic Differentiation for Structural Dynamics

Abstract This work presents a tool that performs simulations in nonlinear vibration analysis. It can be used to appraise the structure's functionality and to determine the loading effects. Oscillations are fundamental in nature, appearing in practical engineering applications. General nonlinear problems hardly have analytical solutions, requiring sophisticated techniques to reach approximate solutions. This toolbox is an open-source Python implementation of a robust multiharmonic balance with predictor–corrector numerical continuation, Newton–Raphson root-solver, and forward automatic differentiation with dual numbers, which is a novelty. It shows promising converging robustness, especially in the construction of frequency response curves, when dealing with polynomial as well as sharp nonlinearities, such as dry-friction.

Receding Horizon Control Using Graph Search for Multi-Agent Trajectory Planning

It is hard to find the global optimum of general nonlinear and nonconvex optimization problems in a reasonable time. This article presents a method to transfer the receding horizon control approach, where nonlinear, nonconvex optimization problems are considered, into graph-search problems. Specifically, systems with symmetries are considered to transfer system dynamics into a finite-state automaton. In contrast to traditional graph-search approaches where the search continues until the goal vertex is found, the transfer of a receding horizon control approach to graph-search problems presented in this article allows to solve them in real time. We prove that the solutions are recursively feasible by restricting the graph search to end in accepting states of the underlying finite-state automaton. The approach is applied to trajectory planning for multiple networked and autonomous vehicles. We evaluate its effectiveness in simulation and experiments in the Cyber-Physical Mobility Lab, an open-source platform for networked and autonomous vehicles. We show real-time capable trajectory planning with collision avoidance in experiments on off-the-shelf hardware and code in MATLAB for two vehicles.

Online Smooth Backfitting for Generalized Additive Models

We propose an online smoothing backfitting method for generalized additive models coupled with local linear estimation. The idea can be extended to general nonlinear optimization problems. The strategy is to use an appropriate-order expansion to approximate the nonlinear equations and store the coefficients as sufficient statistics which can be updated in an online manner by the dynamic candidate bandwidth method. We investigate the statistical and algorithmic convergences of the proposed method. By defining the relative statistical efficiency and computational cost, we further establish a framework to characterize the tradeoff between estimation performance and computation performance. Simulations and real data examples are provided to illustrate the proposed method and algorithm. Supplementary materials for this article are available online.

Bifurcation of solutions of $U(1)$-equivariant semilinear boundary value problems

Assuming that there is a known (trivial) branch of solutions of a parameterized family of equations, topological bifurcation studies the topological invariants of the linearized equations along the trivial branch whose nonvanishing entails the appearance of bifurcation from the trivial branch. We introduce here some refined topological invariants for semilinear elliptic boundary value problems equivariant with respect to the action of the circle $U(1)$ allowing to improve, in this case, some previously obtained bifurcation criteria for general nonlinear elliptic boundary value problems.

Global gradient estimates for general nonlinear elliptic measure data problems with Orlicz growth

We provide an optimal global Calderón-Zygmund theory for gradients of SOLAs to general nonlinear elliptic equations −divA(x,u,Du)=μ whose principle part depends on the solution itself and right-hand data μ is a signed Radon measure. The associated nonlinearity A is assumed to satisfy the (δ,R0)-BMO condition in x, local uniform continuity in u, and Orlicz growth condition in Du, while the boundary of underlying domain is assumed to be Reifenberg flat. This is achieved by employing a perturbation method together with developing a one-parameter technique and by applying the maximal function free technique.

Constrained Consensus-Based Optimization

In this work we are interested in the construction of numerical methods for high-dimensional constrained nonlinear optimization problems by particle-based gradient-free techniques. A consensus-based optimization (CBO) approach combined with suitable penalization techniques is introduced for this purpose. The method relies on a reformulation of the constrained minimization problem in an unconstrained problem for a penalty function and extends to the constrained settings of the class of CBO methods. Exact penalization is employed and, since the optimal penalty parameter is unknown, an iterative strategy is proposed that successively updates the parameter based on the constrained violation. Using a mean-field description of the many particle limit of the arising CBO dynamics, we are able to show convergence of the proposed method to the minimum for general nonlinear constrained problems. Properties of the new algorithm are analyzed. Several numerical examples, also in high dimensions, illustrate the theoretical findings and the good performance of the new numerical method.

Numerical analysis of a nonlinear discrete duality finite volume scheme for Leray-Lions type elliptic problems in Orlicz spaces

In this work, we develop a finite volume approximation for general nonlinear Leray-Lions problems in the Orlicz-Sobolev framework. We prove the existence and uniqueness and some a priori estimate of the approximate solution. We establish a discrete version of Poincaré inequality and a result of discrete compactness which allows us to prove the convergence towards the weak solution of the continuous problem. Some numerical tests are provided on general meshes.

A polygonal element differential method for solving two-dimensional transient nonlinear heat conduction problems

A novel polygonal element differential method (PEDM) is presented for solving two-dimensional nonlinear transient heat conduction problems for the first time. New shape functions as well as their derivatives with respect to isoparametric coordinates are derived to treat the polygonal elements with an internal node. System equations of the PBEM are formulated in terms of the governing equation and heat flux equilibrium condition, in which, a finite difference scheme is executed for calculating the transient term. Then, the nonlinearity system equations are dealt with by the Newton iterative method. Finally, examples with different structural complexities are designed to examine the property of the proposed method. The results show that the PEDM can effectively solve general two-dimensional transient nonlinear heat conduction problems with excellent accuracy and efficiency.

Rigorous solution to second harmonic generation considering transmission and reflection of light at air–crystal interface

Considering the transmission and reflection of TE-polarized pump light at the air–crystal interface, the second harmonic generation (SHG) in a lithium niobate (LN) crystal is investigated theoretically and systematically in this work. In previous studies, the theoretical analyses of reflection and transmission of incident wave in the process of nonlinear frequency conversion were not considered in LN crystal on account of the complicated calculations. First, we establish a physical picture describing that a beam of light in TE mode transports in the LN crystal considering transmission and reflection at the crystal surface and generates nonlinear second-order optical polarization in crystal. Then we analytically derive the reflection coefficient and transmission coefficient of pump light by using the dispersion relationships and electromagnetic boundary conditions. We construct the nonlinear coupled wave equations, derive and present the small signal approximation solution and the general large signal solution exactly. Under the transmission model and reflection model, we find that the conversion efficiency of the second-harmonic wave is obviously dependent on transmission coefficient and other general physical quantities such as the length of LN crystal and the amplitude of pump light. Our analytical theory and formulation can act as an accurate tool for the quantitative evaluation of the SHG energy conversion efficiency in an LN crystal under practical situations, and it can practically be used to treat other more complicated and general nonlinear optics problems.

An energy-based computational scheme for the analysis of reinforced concrete structures with geometric and material nonlinearities

ABSTRACT This paper proposes a new energy-based numerical technique for the nonlinear analysis of reinforced concrete beam elements. The mathematical concept and equations are presented in detail for a general nonlinear quasi-static problem. This method can create a physical sense of the types of energy in a system for an analyst to easily consider any material type and geometric nonlinearity effects from the analysis of reinforced concrete members. The governing equations of this computational scheme have a quadratic form, which leads to proving a complementary relationship for a stability check. In other words, a distinguishing feature of the proposed method is paving the path for selecting the optimum size of displacement increments during a nonlinear numerical analysis. In the proposed method, the steps have been made easier for analysts. Unlike several existing methods, the formulation has also considered the shear and normal deformations, making the analysis results closer to experimental realities. Moreover, the results obtained from solving, evaluating, and comparing several examples demonstrate the acceptable accuracy and convergence of this energy-based approach for reinforced concrete beams. In addition, the quadratic nature of its formulation provides an analyst with information about the accuracy and stability of the obtained structural responses.