Modified and a new spectral method for solving nonlinear ordinary differential equations

Resonance method commonly used for Painleve classification of ordinary differential equations often detects negative resonances, other than a trivial one of –1 (which should always be present). However, it is asserted by some researchers that the nature of these negative resonances is not fully understood. The problem arises as how to use negative nontrivial resonances to obtain information about the analytical properties of solutions of non-linear differential equations, in particular, to construct rational solutions. In the present paper, the method for constructing rational solutions f certain classes of autonomous non-linear ordinary differential equations is presented.

The approximations of the nonlinear heat transport problem are based on the finite volume (FM) and averaging (AM) methods [1,2]. This procedures allows reduce the nonlinear 2‐D problem for partial differential equation (PDE) to a initial‐value problem for a system of 2 nonlinear ordinary differential equations(ODE) of first order in the time t or to a initial‐value problem for one nonlinear ODE of first order with two nonlinear algebraic equations.

This thesis aims at developing efficient mathematical methods for solving hierarchical dynamic optimization problems. The main motivation is to model processes in nature, for which there is evidence to assume that they run optimally. We describe models of such processes by optimal control problems (called optimal control models (OCMs)). However, an OCM typically includes unknown parameters that cannot be derived entirely on a theoretical basis, which is in particular the case for the cost function. Therefore, we develop parameter estimation techniques to estimate the unknowns in an OCM from observation data of the process. Mathematically, this leads to a hierarchical dynamic optimization problem with a parameter estimation problem on the upper level and an optimal control problem on the lower level. We focus on multi-stage equality and inequality constrained optimal control problems based on nonlinear ordinary differential equations. The main goal of this thesis is to derive numerically efficient mathematical methods for solving hierarchical dynamic optimization problems, and to use these methods to estimate parameters in high-dimensional OCMs from real-world measurement data. We develop parameter-dependent OCMs for the gait of cerebral palsy patients and able-bodied subjects. The unknown parameters in the OCMs are then estimated from real-world motion capture data provided by the Heidelberg MotionLab of the Orthopedic University Clinic Heidelberg by using the mathematical methods developed within this work. The main novelties and contributions of this thesis to the field of hierarchical dynamic optimization are summarized herein. - We establish a novel mathematical method, a so-called direct all-at-once approach, for solving hierarchical dynamic optimization problems based on the direct multiple shooting method and first-order optimality conditions. - Furthermore, we propose an efficient numerical algorithm for large-scale hierarchical dynamic optimization problems, which fully exploits the structures inherited from both the hierarchical setting and the discretization. - Pontryagin's maximum principle is used to analyze solution properties of hierarchical dynamic optimization problems like second-order optimality conditions of the lower-level problem. - In addition, we propose and discuss alternative methods for hierarchical dynamic optimization that are based on derivative-free optimization and a bundle approach. These methods keep the hierarchical problem setting and do not reformulate the lower-level problem using first-order optimality conditions. - We establish a novel lifting method for regularizing mathematical programs with complementarity constraints, which is discussed and numerically investigated by means of a well-known collection of benchmark problems. - Proofs of regularity and convergence results for sequential quadratic programming methods applied to lifted mathematical programs with complementarity constraints are provided. - Efficient state-of-the-art implementations of all mathematical methods derived in this thesis, as well as a benchmark collection of hierarchical dynamic optimization problems are presented. - High-dimensional optimal control gait models for cerebral palsy patients and able-bodied subjects are developed. The mathematical methods derived in this thesis are used to estimate the unknown model parameters from real-world motion capture data provided by the Heidelberg MotionLab of the Orthopedic University Clinic Heidelberg. The theoretical and practical results presented in this thesis can be considered an initial motivating step towards answering open questions in current medical research in fields like treatment planning, classification of gaits, or the evaluation of surgeries by means of hierarchical dynamic optimization.

Solution of nonlinear partial differential equations from base equations

This thesis has addressed some of the limitations of current methods that are used to estimate parameters of ODEs and developed novel and efficient methods in this context. These methods facilitate inference by tackling some of the challenges that appear when conducting estimation for ODEs, such as computational time, multimodality and quantification of the uncertainty in parameter values.

Adomian Decomposition Method (ADM) is of a great important to mathematicians nowadays. In this paper, ADM and Kutta-4-Method were applied to nonlinear systems of ordinary differential equations. From a comparison study, it was found that ADM offers a simple and more accurate approximate solution. Also, a higher-order nonlinear ordinary differential equation was transformed to the corresponding system of equations. Both equation and corresponding system were solved. A comparison study using ADM shows that, this transformation simplifies calculations.

An analysis is carried out to study the effect of the magnetic field on the boundary layer flow of the nanofluids over an isothermal stretching surface. Two types of nanofluids namely Ag–water and Cu– water are considered. Similarity transformation is used to convert the governing nonlinear equations into coupled higher order nonlinear ordinary differential equations. Fourth-order Runge–Kutta method with shooting technique is employed for the numerical solution of the obtained equations. Numerical results are obtained for distribution of velocity, temperature and concentration, for both cases .The numerical results indicate that an increase in the nanoparticle volume fraction will decrease the velocity boundary layer thickness. Meanwhile, the presence of nanoparticles results in an increase in the magnitude of the skin friction along the surface. Such effects are found to be more evident in the Ag–water solution than in the Cu–water solution. The obtained numerical results have been presented graphically and discussed in details.

Summary Gradient reproducing kernel particle method (GRKPM) is a meshless technique which incorporates the first gradients of the function into the reproducing equation of RKPM. Therefore, in two-dimensionalspace GRKPM introducesthree types of shape functions rather than one. The robustness of GRKPM’s shape functions is established by reconstruction of a third-order polynomial. To enforce the essential boundary conditions (EBCs), GRKPM’s shape functions are modified by transformation technique. By utilizing the modified shape functions, the weak form of the nonlinear evolutionary Buckley-Leverett (BL) equation is discretized in space, rendering a system of nonlinear ordinary differential equations (ODEs). Subsequently, Gear’s method is applied for temporal discretization of the ODEs. Through numerical experiments, employment of a moderate viscosity seeks the efficacy ofthesolutionwhen thediffusionterm isimportant;moreover, applicationof a small viscosity confirms the potential of the approach for treatment of the problems involving steep gradient regions. The outcomes are verified by performing convergence tests using uniformly spaced particles. Consideration of non-uniform distribution of particles further demonstrates the virtue of the presented methodology in producing smooth profiles in the critical regions near the fronts.

The purpose of this paper is to investigate the magnetohydrodynamic stability external flow of a viscous, incompressible and electrically conducting fluid over a moving flat plate using temporal linear stability analysis. Using a similarity variables based on the Skan-Falkner transformation, the governing differential equations of mean flow are transformed into a nonlinear ordinary differential equation, which is then solved numerically by the Runge-Kutta method. The MHD stability equation is solved numerically by using the Chebyshev spectral collocation method, which is based on the eigenfunction expansion in terms of Chebyshev polynomials, collocation points, and the subsequent solution of the resulting generalized eigenvalue problem with the QZ algorithm. The influence of the pressure gradient, magnetic field and wall velocity on the dimensionless mean velocity are presented graphically and discussed. For the disturbance flow in the presence of magnetic field and moving wall, the critical Reynolds number increases with increasing the magnetic field parameter and wall velocity, indicating that these parameters stabilize the flow.

COHO is a verification tool for systems modeled by nonlinear ordinary differential equations (ODEs). Verification is performed using reachability analysis. The reachable space is represented by projectagons which are the polyhedron described by their projection onto two dimensional subspace. COHO approximates nonlinear ODEs with linear differential inclusions to compute the reachable state. We re-engineer the COHO system to provide a robust implementation with a clear interface and well-organized structure. We demonstrate the soundness and robustness of our approach by applying it to several examples, including a three dimensional, non-linear systems for which previous version of COHO failed due to numerical stability problems. The correctness of COHO strongly depends on the accuracy, efficiency and robustness of the linear program solver that is used throughout the analysis. Our COHO linear program solver is implemented by integrating the Simplex algorithm with interval arithmetic based on the framework set up by Laza [Laz01]. For highly ill-conditioned problems, an approximation that is very close to the optimal result is computed by our algorithm. This results in a very small error bound that allows COHO to successfully verify interesting examples. This thesis also presents an algorithm to solve the problem of projecting the feasible region of a linear program onto two dimensional subspaces. This algorithm uses the COHO linear program solver for efficiency and accuracy. We derive an analytical upper bound for the error and present experimental results to show that the errors are negligible in practice.

A topic of interest in financial mathematics is the Black-Scholes model. However, the underlying asset price in the stock market may not be satisfied by this linear model, which was developed under a number of assumptions, including liquidity and the absence of transaction costs. The linear model has restricted its precision in actual market conditions. We study the transaction cost model for modelling illiquid markets from the extended nonlinear Black-Scholes model. Using a semi-discretization finite difference approach, the nonlinear partial differential equation is transformed into a nonlinear ordinary differential equation. Deep Learning (DL) is an advanced technique of machine learning solves the converted ordinary differential equation by fully connected neural network (FCNN). By modelling the complex and nonlinear relationships among market variables, DL models can generate option pricing forecasts that are more dependable and precise, not just for continuous data but also for discontinuous data (at jump point). Introduction: Nonlinear partial differential equation plays a crucial role in financial modelling, especially in the pricing of derivatives like options. The Black- Scholes model, introduced in 1973, continues to be one of the most widely used frameworks for pricing European options. However, this model is a linear one and offers an analytical solution, yet it is not appropriate for the complexities of real market assumptions that exhibit nonlinear effects. We examine the transaction cost model for modelling illiquid markets from the extended nonlinear Black-Scholes model created by Seelama et al. (2021). First using a semi-discretization finite difference approach, the nonlinear partial differential equation is transformed into a nonlinear ordinary differential equation. Solves the converted ordinary differential equation by Deep Learning (DL) based fully connected neural network (FCNN) algorithm. This algorithm is capable of handling the nonlinear behaviour of model and produce more accurate option value for European call. Objectives: Find the solution of more realistic nonlinear model of Black-Scholes equation include transaction cost in illiquid market with deep learning algorithm for a European call option. Methods: From extended nonlinear Black-Scholes model, the nonlinear model of transaction cost in illiquid market is considered for study. The nonlinear partial differential equation is converted into a nonlinear ordinary differential equation by semi-discretization finite difference method. DL is a sophisticated machine learning technique that solves transformed ordinary differential equations using fully connected neural network (FCNN) algorithm. DL algorithm uses a Python program. Results: For European call option, option values are predicted for different number of neurons with different loss functions like MSE and MAE at the time of maturity. Graphical representation shows the accuracy of the algorithm at continuous as well as at jump point (strike price). Conclusions: The method of solving a complex nonlinear partial differential equation by transforming it into a nonlinear ordinary differential equation is valuable. More precise pricing estimates for option values with nonlinear effects in financial data could be improved by deep learning.

Micro-cantilever beams are interested structures in MEMS because of their fabrication is very easy and its versatility. The importance of micro-cantilevers beam in MEMS has driven various investigations like static and dynamic performances under different loading such as potential fields. In this research the non-linear differential equation which models dynamics of a micro-cantilever beams vibration subjected to electrostatic field has been studied. The model which has one degree of freedom is used to calculate the pull-in voltage. This model adopted based on different method of calculating stiffness of micro-cantilever beam. The nonlinear ordinary differential equation which used to model the dynamics of the cantilever subjected to electric field close to snap on is highly stiff. Investigation on solving of nonlinear stiff ordinary equation showed that only Lsode algorithm yield to correct solution to the problem. Lsode is equipped with a robust adaptive time step selection mechanism that enables solutions to very stiff problems, as the one under discussion. The best match in the resonant frequency for equivalent stiffness based on four different models was considered. The stiffness model suitable for the best match in deflection is proved to be different from the model that yields. Pull-in voltage under electric field was studied. Pull-in voltage has been investigated from the analytical and numerical perspective. A complete parametric study of structural damping effect on large deflection of micro-cantilever beam was studied was done numerically in this work. Different kind of impulse voltages were considered and effect of them on pulling voltage numerically was studied. A cumbersome mathematical method, Lie symmetry, was used to drive a closed from of time response to step voltage for undamped system and pull in voltage of such system was calculated. Finally, a closed form driven from the nonlinear ODE for calculating pulling voltage was presented.

Non-linear vibration and instability of multi-phase composite plate subjected to non-uniform in-plane parametric excitation: Semi-analytical investigation

On considere le comportement oscillatoire des solutions de l'equation differentielle non lineaire d'ordre 2 avec un amortissement (r(t)x')'+p(t)x'+q(t)f(x)=0, t∈[0,∞], ou r, p, q ∈C[0,∞], r(t)>0, et p, q peuvent prendre des valeurs negatives pour t arbitraitement grand, f∈C(R), xf(x)>0 pour x¬=0

Positive solutions of a class of nonlinear ordinary differential equations