Methods For Ordinary Differential Equations Research Articles

Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion

We provide a comparative analysis of qualitative features of different numerical methods for the inhomogeneous geometric Brownian motion (IGBM). The limit distribution of the IGBM exists, its conditional and asymptotic mean and variance are known and the process can be characterised according to Feller’s boundary classification. We compare the frequently used Euler–Maruyama and Milstein methods, two Lie–Trotter and two Strang splitting schemes and two methods based on the ordinary differential equation (ODE) approach, namely the classical Wong–Zakai approximation and the recently proposed log-ODE scheme. First, we prove that, in contrast to the Euler–Maruyama and Milstein schemes, the splitting and ODE schemes preserve the boundary properties of the process, independently of the choice of the time discretisation step. Second, we prove that the limit distribution of the splitting and ODE methods exists for all stepsize values and parameters. Third, we derive closed-form expressions for the conditional and asymptotic means and variances of all considered schemes and analyse the resulting biases. While the Euler–Maruyama and Milstein schemes are the only methods which may have an asymptotically unbiased mean, the splitting and ODE schemes perform better in terms of variance preservation. The Strang schemes outperform the Lie–Trotter splittings, and the log-ODE scheme the classical ODE method. The mean and variance biases of the log-ODE scheme are very small for many relevant parameter settings. However, in some situations the two derived Strang splittings may be a better alternative, one of them requiring considerably less computational effort than the log-ODE method. The proposed analysis may be carried out in a similar fashion on other numerical methods and stochastic differential equations with comparable features.

Quantum homotopy perturbation method for nonlinear dissipative ordinary differential equations

While quantum computing provides an exponential advantage in solving linear differential equations, there are relatively few quantum algorithms for solving nonlinear differential equations. In our work, based on the homotopy perturbation method, we propose a quantum algorithm for solving n-dimensional nonlinear dissipative ordinary differential equations (ODEs). Our algorithm first converts the original nonlinear ODEs into the other nonlinear ODEs which can be embedded into finite-dimensional linear ODEs. Then we solve the embedded linear ODEs with quantum linear ODEs algorithm and obtain a state ϵ-close to the normalized exact solution of the original nonlinear ODEs with success probability Ω(1). The complexity of our algorithm is O(gηT poly(log(nT/ϵ))), where η, g measure the decay of the solution. Our algorithm provides exponential improvement over the best classical algorithms or previous quantum algorithms in n or ϵ.

Convergence of multilevel spectral deferred corrections

The spectral deferred correction (SDC) method is class of iterative solvers for ordinary differential equations (ODEs). It can be interpreted as a preconditioned Picard iteration for the collocation problem. The convergence of this method is well-known, for suitable problems it gains one order per iteration up to the order of the quadrature method of the collocation problem provided. This appealing feature enables an easy creation of flexible, high-order accurate methods for ODEs. A variation of SDC are multi-level spectral deferred corrections (MLSDC). Here, iterations are performed on a hierarchy of levels and an FAS correction term, as in nonlinear multigrid methods, couples solutions on different levels. While there are several numerical examples which show its capabilities and efficiency, a theoretical convergence proof is still missing. This paper addresses this issue. A proof of the convergence of MLSDC, including the determination of the convergence rate in the time-step size, will be given and the results of the theoretical analysis will be numerically demonstrated. It turns out that there are restrictions for the advantages of this method over SDC regarding the convergence rate.

A Functionally-Fitted Block Numerov Method for Solving Second-Order Initial-Value Problems with Oscillatory Solutions

A functionally-fitted Numerov-type method is developed for the numerical solution of second-order initial-value problems with oscillatory solutions. The basis functions are considered among trigonometric and hyperbolic ones. The characteristics of the method are studied, particularly, it is shown that it has a third order of convergence for the general second-order ordinary differential equation, y''=f left( x,y,y' right) , it is a fourth order convergent method for the special second-order ordinary differential equation, y''=f left( x,yright) . Comparison with other methods in the literature, even of higher order, shows the good performance of the proposed method.

Least Square Homotopy Perturbation Method for Ordinary Differential Equations

In this study, a new modification of the homotopy perturbation method (HPM) is introduced for various order boundary value problems (BVPs). In this modification, HPM is hybrid with least square optimizer and named as the least square homotopy perturbation method (LSHPM). The proposed scheme is tested against various linear and nonlinear BVPs (second to seventh order DEs). Validity of the obtained solutions is confirmed by finding absolute errors. To analyze the efficiency of the proposed scheme, tested problems have also been solved through HPM and results are compared with LSHPM. Furthermore, obtained results are also compared with other numerical schemes available in literature. Analysis reveals that LSHPM is a consistent and effective scheme which can be used for more complex BVPs in science and engineering.

Learning Dynamic Systems Using Gaussian Process Regression with Analytic Ordinary Differential Equations as Prior Information

Recently the data-driven learning of dynamic systems has become a promising approach because no physical knowledge is needed. Pure machine learning approaches such as Gaussian process regression (GPR) learns a dynamic model from data, with all physical knowledge about the system discarded. This goes from one extreme, namely methods based on optimizing parametric physical models derived from physical laws, to the other. GPR has high flexibility and is able to model any dynamics as long as they are locally smooth, but can not generalize well to unexplored areas with little or no training data. The analytic physical model derived under assumptions is an abstract approximation of the true system, but has global generalization ability. Hence the optimal learning strategy is to combine GPR with the analytic physical model. This paper proposes a method to learn dynamic systems using GPR with analytic ordinary differential equations (ODEs) as prior information. The one-time-step integration of analytic ODEs is used as the mean function of the Gaussian process prior. The total parameters to be trained include physical parameters of analytic ODEs and parameters of GPR. A novel method is proposed to simultaneously learn all parameters, which is realized by the fully Bayesian GPR and more promising to learn an optimal model. The standard Gaussian process regression, the ODE method and the existing method in the literature are chosen as baselines to verify the benefit of the proposed method. The predictive performance is evaluated by both one-time-step prediction and long-term prediction. By simulation of the cart-pole system, it is demonstrated that the proposed method has better predictive performances.

On eigenvalues of a matrix arising in energy-preserving/dissipative continuous-stage Runge-Kutta methods

Abstract In this short note, we define an s × s matrix Ks constructed from the Hilbert matrix H s = ( 1 i + j - 1 ) i , j = 1 s {H_s} = \left( {{1 \over {i + j - 1}}} \right)_{i,j = 1}^s and prove that it has at least one pair of complex eigenvalues when s ≥ 2. Ks is a matrix related to the AVF collocation method, which is an energy-preserving/dissipative numerical method for ordinary differential equations, and our result gives a matrix-theoretical proof that the method does not have large-grain parallelism when its order is larger than or equal to 4.

Influence of gold electrode layers on the SH waves propagation in sandwich structures based on Au/PMN-0.33PT/Au and Au/PZT-5H/Au: Numerical implementation

This paper discusses the effects induced by the gold electrodes on shear horizontal (SH) wave propagating through a sandwich structures. A combination of the ordinary differential equation (ODE) and stiffness matrix (SM) methods is employed to calculate the dispersion curves, mechanical displacements and stresses of SH modes. Formulations are given only for the short-circuit (SC) surface. To facilitate understanding of the SH dispersion curves trajectories; a piezoelectric middle layer change study is performed moving from PMN-0.33PT to PZT-5H. The middle layer is shown to have a significant influence on phase velocities. Moreover, it was found that thickness, mass density and elastic constants of the gold electrode have a significant effect on the characteristics of SH waves. The displacements and stresses of SH modes are analyzed. From the scientific perspective of smart materials, the results are useful for understanding and optimization of new designs for acoustic devices made of piezoelectric materials.

BLUES function method applied to partial differential equations and analytic approximants for interface growth under shear

An iteration sequence based on the BLUES (beyond linear use of equation superposition) function method is presented for calculating analytic approximants to solutions of nonlinear partial differential equations. This extends previous work using this method for nonlinear ordinary differential equations with an external source term. Now, the initial condition plays the role of the source. The method is tested on three examples: a reaction-diffusion-convection equation, the porous medium equation with growth or decay, and the nonlinear Black-Scholes equation. A comparison is made with three other methods: the Adomian decomposition method (ADM), the variational iteration method (VIM), and the variational iteration method with Green function (GVIM). As a physical application, a deterministic differential equation is proposed for interface growth under shear, combining Burgers and Kardar-Parisi-Zhang nonlinearities. Thermal noise is neglected. This model is studied with Gaussian and space-periodic initial conditions. A detailed Fourier analysis is performed and the analytic coefficients are compared with those of ADM, VIM, GVIM, and standard perturbation theory. The BLUES method turns out to be a worthwhile alternative to the other methods. The advantages that it offers ensue from the freedom of choosing judiciously the linear part, with associated Green function, and the residual containing the nonlinear part of the differential operator at hand.

Solitonic interactions for Rossby waves with the influence of Coriolis parameters

In the current study, the Rossby waves envelope near the equator is studied starting from a potential vorticity equation with the horizontal component of Coriolis parameter. Based on the equation, we first establish the amplitude evolution equation of the Rossby waves with complete Coriolis parameter and obtain rational function solutions by means of the subsidiary ordinary differential equation method with the help of chirp-free hypothesis. Then the 1-, 2-, 3-and N-soliton solutions are also obtained by the Hirota method. For these obtained solutions, they are rarely mentioned in fluid mechanics of ocean and atmospheric motion. On the other hand, these three-dimensional stereograms of the 1-, 2-soliton solutions are depicted with the physical parameter changing. The influence of each parameter on wave amplitude, the peak position and propagation direction of Rossby waves are discussed. The results show that the horizontal Coriolis parameter only alter the phase shift of the Rossby waves amplitude for the 1-soliton and 2-soliton solution. These results are of great significance for further theoretical research in geophysical fluid.

Plane Wave Solutions With Dynamical Behaviors for Heisenberg Model of Ferromagnetic Spin Chain With Beta Derivative Evolution and Obliqueness

The plane waves with their dynamical behaviors of (2+1)-dimensional nonlinear Schrodinger equation (NLSE) having beta derivative spatial-temporal evolution are investigated. In order to study such phenomena, NLSE is converted to a nonlinear ordinary differential equation with a planar dynamical system (PDS) by considering the variable wave transform and the properties of the beta derivative. By employing two distinct solution methods, namely, the auxiliary ordinary differential equation method and the extended simplest equation method, some more new general form of analytical solutions of NLSE are constructed. The effect of beta derivative parameter and obliqueness on several types of wave structures along with the phase portrait diagrams are reported. It is found that the PDS is not supported by any type of orbits for Θ = 45°. It is also confirmed from the obtained solutions that no plane waves are generated for Θ = 45°. The presented studies on bifurcation analysis and analytical solutions for (2+1)-dimensional NLSE would be very useful to understand the physical scenarios for Heisenberg models of ferromagnetic spin chain with magnetic interactions.

An Introduction to Development of Centralized and Distributed Stochastic Approximation Algorithm with Expanding Truncations

The stochastic approximation algorithm (SAA), starting from the pioneer work by Robbins and Monro in 1950s, has been successfully applied in systems and control, statistics, machine learning, and so forth. In this paper, we will review the development of SAA in China, to be specific, the stochastic approximation algorithm with expanding truncations (SAAWET) developed by Han-Fu Chen and his colleagues during the past 35 years. We first review the historical development for the centralized algorithm including the probabilistic method (PM) and the ordinary differential equation (ODE) method for SAA and the trajectory-subsequence method for SAAWET. Then, we will give an application example of SAAWET to the recursive principal component analysis. We will also introduce the recent progress on SAAWET in a networked and distributed setting, named the distributed SAAWET (DSAAWET).

The Speed of Traveling Waves in a FKPP-Burgers System

We consider a coupled reaction-advection-diffusion system based on the Fisher-KPP and Burgers equations. These equations serve as a one-dimensional version of a model for a reacting fluid in which the arising density differences induce a buoyancy force advecting the fluid. We study front propagation in this system through the lens of traveling waves solutions. We are able to show two quite different behaviors depending on whether the coupling constant $\rho$ is large or small. First, it is proved that there is a threshold $\rho_0$ under which the advection has no effect on the speed of traveling waves (although the advection is nonzero). Second, when $\rho$ is large, wave speeds must be at least $\mathcal{O}(\rho^{1/3})$. These results together give that there is a transition from pulled to pushed waves as $\rho$ increases. Because of the complex dynamics involved in this and similar models, this is one of the first precise results in the literature on the effect of the coupling on the traveling wave solution. We use a mix of ordinary and partial differential equation methods in our analytical treatment, and we supplement this with a numerical treatment featuring newly created methods to understand the behavior of the wave speeds. Finally, various conjectures and open problems are formulated.

Solution of Parabolic Navier-Stokes Equations for The Entrance Region-Flow between Two Parallel Plates.(Dept.M)

The development of the flow field in the entrance region is theoretically examined, for the case of flow between two parallel flat plates. The flow field is described by the parabolic Navier-Stokes equations. Because of the nature of these equations; it is convenient to solve them by the application of the local similarity solution method. According to this method, dimensionless form of momentum equations are transferred to ordinary differential equations. These two modified equations are solved numerically by Runge-Kutta method of ordinary differential equations accompanied with shooting method of boundary value problems. The values of coefficient of friction are calculated at different positions along the passage. Also the velocity profile at any position, according to this approach, can be obtained. Three passages, in this work, are studied; with Reynolds number ( based on the half of the height of the passage) of 100, 200 and 300.

Modeling the effect of application-specific program transformations on energy and performance improvements of parallel ODE solvers

Ordinary differential equations (ODEs) are important for modelling many problems from science and engineering and efficient ODE solvers are required, for example when solving time-dependent partial differential equations (PDEs) with the method of lines. Since an ODE solver may perform a large number of iteration steps, the execution time for solving an ODE problem might be quite large. Thus, a reduction of the execution time is desirable and should affect each iteration step of the simulation. Programming techniques to reduce the execution time of ODE solver are parallelism and modification of the memory access structure such that the memory access time decreases. In this article, we investigate multithreaded solution methods for ODEs with different memory access behavior and their influence on the performance. Additionally the energy consumption is considered. The parallelism is implemented as shared memory program for multicore processors. The memory access behavior is investigated using different program variants which result from application-specific program transformations changing the memory access order while guaranteeing the numerical correctness. For the investigation of the performance, experimental data have been gathered on five different recent multicore processors. Additionally, an analytical power and energy model for modeling the performance and energy consumption is introduced. As ODE solver, the popular embedded Runge-Kutta methods with error correction is used. The simulation problems are two different ODEs resulting from discretized PDEs. The experimental data give insight into the quite diverse performance behavior of the ODE solver variants solving the same problem on different platforms.

Classical Theory of Linear Multistep Methods for Volterra Functional Differential Equations

Based on the linear multistep methods for ordinary differential equations (ODEs) and the canonical interpolation theory that was presented by Shoufu Li who is exactly the second author of this paper, we propose the linear multistep methods for general Volterra functional differential equations (VFDEs) and build the classical stability, consistency, and convergence theories of the methods. The methods and theories presented in this paper are applicable to nonneutral, nonstiff, and nonlinear initial value problems in ODEs, Volterra delay differential equations (VDDEs), Volterra integro-differential equations (VIDEs), Volterra delay integro-differential equations (VDIDEs), etc. At last, some numerical experiments verify the correctness of our theories.

Two-dimensional incremental recursive least squares identification for batch processes

In this paper, two-dimensional incremental recursive least squares identification methods for batch processes are proposed. First, parameter increment along the time direction is introduced to represent the batch process, which changes slowly along the batch direction in comparison with the parameters. Then, we apply RLS method along the batch direction to derive two kinds of two-dimensional incremental recursive identification algorithm, which can use the latest estimation results along both the time direction and batch direction to obtain the new estimated value. By extending the model assumption from parameter batch-invariant to parameter increment batch-invariant, this method can effectively accelerate the convergence rate. Furthermore, the convergence property of the proposed method is established by the associated ordinary differential equation method. Finally, we demonstrate the superiority of the proposed identification method in two case studies.

DeC and ADER: Similarities, Differences and a Unified Framework

In this paper, we demonstrate that the explicit ADER approach as it is used inter alia in [1] can be seen as a special interpretation of the deferred correction (DeC) method as introduced in [2]. By using this fact, we are able to embed ADER in a theoretical background of time integration schemes and prove the relation between the accuracy order and the number of iterations which are needed to reach the desired order. Next, we extend our investigation to stiff ODEs, treating these source terms implicitly. Some differences in the interpretation and implementation can be found. Using DeC yields typically a much simpler implementation, while ADER benefits from a higher accuracy, at least for our numerical simulations. Then, we also focus on the PDE case and present common space-time discretizations using DeC and ADER in closed forms. Finally, in the numerical section we investigate A-stability for the ADER approach - this is done for the first time up to our knowledge - for different order using several basis functions and compare them with the DeC ansatz. Then, we compare the performance of ADER and DeC for stiff and non-stiff ODEs and verify our analysis focusing on two basic hyperbolic problems. [1] O. Zanotti, F. Fambri, M. Dumbser, and A. Hidalgo. Space-time adaptive ader discontinuous galerkin finite element schemes with a posteriori sub-cell finite volume limiting. Computers & Fluids, 118:204-224, 2015. [2] A. Dutt, L. Greengard, and V. Rokhlin. Spectral Deferred Correction Methods for Ordinary Differential Equations. BIT Numerical Mathematics, 40(2):241-266, 2000.

Solution of the Multigroup Neutron Diffusion Eigenvalue Problem in Slab Geometry by Modified Power Method

We describe in this work the application of the modified power method for solve the multigroup neutron diffusion eigenvalue problem in slab geometry considering two-dimensions for nuclear reactor global calculations. It is well known that criticality calculations can often be best approached by solving eigenvalue problems. The criticality in nuclear reactors physics plays a relevant role since establishes the ratio between the numbers of neutrons generated in successive fission reactions. In order to solve the eigenvalue problem, a modified power method is used to obtain the dominant eigenvalue (effective multiplication factor) and its corresponding eigenfunction (scalar neutron flux), which is non-negative in every domain, that is, physically relevant. The innovation of this work is solving the neutron diffusion equation in analytical form for each new iteration of the power method. For solve this problem we propose to apply the Finite Fourier Sine Transform on one of the variables obtaining a transformed problem which is resolved by well-established methods for ordinary differential equations. The inverse Fourier Transform is used to reconstruct the solution for the original problem. It is known that the power method is an iterative source method in which is updated by the neutron flux expression of previous iteration. Thus, for each new iteration, the neutron flux expression becomes larger and more complex due to analytical solution what makes us propose that it be reconstructed through a polynomial interpolation. The methodology is implemented to solve a homogeneous problem and the results are compared with works presents in the literature.

Comparison of numerical methods to price zero coupon bonds in a two-factor CIR model

In this paper we price a zero coupon bond under a Cox–Ingersoll–Ross (CIR) two-factor model using various numerical schemes. To the best of our knowledge, a closed-form or explicit price functional is not trivial and has been less studied. The use and comparison of several numerical methods to determine the bond price is one contribution of this paper. Ordinary differential equations (ODEs) , finite difference schemes and simulation are the three classes of numerical methods considered. These are compared on the basis of computational efficiency and accuracy, with the second aim of this paper being to identify the most efficient numerical method. The numerical ODE methods used to solve the system of ODEs arising as a result of the affine structure of the CIR model are more accurate and efficient than the other classes of methods considered, with the Runge–Kutta ODE method being the most efficient. The Alternating Direction Implicit (ADI) method is the most efficient of the finite difference scheme methods considered, while the simulation methods are shown to be inefficient. Our choice of considering these methods instead of the other known and apparently new numerical methods (eg Fast Fourier Transform (FFT) method, Cosine (COS) method, etc.) is motivated by their popularity in handling interest rate instruments.
 Keywords: Cox–Ingersoll–Ross model; numerical methods; Runge–Kutta method; zero-coupon bonds; Alternating Direction Implicit method