Homotopy analysis method

Last updated
The two dashed paths shown above are homotopic relative to their endpoints. The animation represents one possible homotopy. HomotopySmall.gif
The two dashed paths shown above are homotopic relative to their endpoints. The animation represents one possible homotopy.

The homotopy analysis method (HAM) is a semi-analytical technique to solve nonlinear [ disambiguation needed ] ordinary/partial differential equations. The homotopy analysis method employs the concept of the homotopy from topology to generate a convergent series solution for nonlinear systems. This is enabled by utilizing a homotopy-Maclaurin series to deal with the nonlinearities in the system.

Contents

The HAM was first devised in 1992 by Liao Shijun of Shanghai Jiaotong University in his PhD dissertation [1] and further modified [2] in 1997 to introduce a non-zero auxiliary parameter, referred to as the convergence-control parameter, c0, to construct a homotopy on a differential system in general form. [3] The convergence-control parameter is a non-physical variable that provides a simple way to verify and enforce convergence of a solution series. The capability of the HAM to naturally show convergence of the series solution is unusual in analytical and semi-analytic approaches to nonlinear partial differential equations.

Characteristics

The HAM distinguishes itself from various other analytical methods in four important aspects. First, it is a series expansion method that is not directly dependent on small or large physical parameters. Thus, it is applicable for not only weakly but also strongly nonlinear problems, going beyond some of the inherent limitations of the standard perturbation methods. Second, the HAM is a unified method for the Lyapunov artificial small parameter method, the delta expansion method, the Adomian decomposition method, [4] and the homotopy perturbation method. [5] [6] The greater generality of the method often allows for strong convergence of the solution over larger spatial and parameter domains. Third, the HAM gives excellent flexibility in the expression of the solution and how the solution is explicitly obtained. It provides great freedom to choose the basis functions of the desired solution and the corresponding auxiliary linear operator of the homotopy. Finally, unlike the other analytic approximation techniques, the HAM provides a simple way to ensure the convergence of the solution series.

The homotopy analysis method is also able to combine with other techniques employed in nonlinear differential equations such as spectral methods [7] and Padé approximants. It may further be combined with computational methods, such as the boundary element method to allow the linear method to solve nonlinear systems. Different from the numerical technique of homotopy continuation, the homotopy analysis method is an analytic approximation method as opposed to a discrete computational method. Further, the HAM uses the homotopy parameter only on a theoretical level to demonstrate that a nonlinear system may be split into an infinite set of linear systems which are solved analytically, while the continuation methods require solving a discrete linear system as the homotopy parameter is varied to solve the nonlinear system.

Applications

In the last twenty years, the HAM has been applied to solve a growing number of nonlinear ordinary/partial differential equations in science, finance, and engineering. [8] [9] For example, multiple steady-state resonant waves in deep and finite water depth [10] were found with the wave resonance criterion of arbitrary number of traveling gravity waves; this agreed with Phillips' criterion for four waves with small amplitude. Further, a unified wave model applied with the HAM, [11] admits not only the traditional smooth progressive periodic/solitary waves, but also the progressive solitary waves with peaked crest in finite water depth. This model shows peaked solitary waves are consistent solutions along with the known smooth ones. Additionally, the HAM has been applied to many other nonlinear problems such as nonlinear heat transfer, [12] the limit cycle of nonlinear dynamic systems, [13] the American put option, [14] the exact Navier–Stokes equation, [15] the option pricing under stochastic volatility, [16] the electrohydrodynamic flows, [17] the Poisson–Boltzmann equation for semiconductor devices, [18] and others.

Brief mathematical description

An isotopy of a coffee cup into a doughnut (torus). Mug and Torus morph.gif
An isotopy of a coffee cup into a doughnut (torus).

Consider a general nonlinear differential equation

,

where is a nonlinear operator. Let denote an auxiliary linear operator, u0(x) an initial guess of u(x), and c0 a constant (called the convergence-control parameter), respectively. Using the embedding parameter q ∈ [0,1] from homotopy theory, one may construct a family of equations,

called the zeroth-order deformation equation, whose solution varies continuously with respect to the embedding parameter q ∈ [0,1]. This is the linear equation

with known initial guess U(x; 0) = u0(x) when q = 0, but is equivalent to the original nonlinear equation , when q = 1, i.e. U(x; 1) = u(x)). Therefore, as q increases from 0 to 1, the solution U(x; q) of the zeroth-order deformation equation varies (or deforms) from the chosen initial guess u0(x) to the solution u(x) of the considered nonlinear equation.

Expanding U(x; q) in a Taylor series about q = 0, we have the homotopy-Maclaurin series

Assuming that the so-called convergence-control parameter c0 of the zeroth-order deformation equation is properly chosen that the above series is convergent at q = 1, we have the homotopy-series solution

From the zeroth-order deformation equation, one can directly derive the governing equation of um(x)

called the mth-order deformation equation, where and for k > 1, and the right-hand side Rm is dependent only upon the known results u0, u1, ..., um  1 and can be obtained easily using computer algebra software. In this way, the original nonlinear equation is transferred into an infinite number of linear ones, but without the assumption of any small/large physical parameters.

Since the HAM is based on a homotopy, one has great freedom to choose the initial guess u0(x), the auxiliary linear operator , and the convergence-control parameter c0 in the zeroth-order deformation equation. Thus, the HAM provides the mathematician freedom to choose the equation-type of the high-order deformation equation and the base functions of its solution. The optimal value of the convergence-control parameter c0 is determined by the minimum of the squared residual error of governing equations and/or boundary conditions after the general form has been solved for the chosen initial guess and linear operator. Thus, the convergence-control parameter c0 is a simple way to guarantee the convergence of the homotopy series solution and differentiates the HAM from other analytic approximation methods. The method overall gives a useful generalization of the concept of homotopy.

The HAM and computer algebra

The HAM is an analytic approximation method designed for the computer era with the goal of "computing with functions instead of numbers." In conjunction with a computer algebra system such as Mathematica or Maple, one can gain analytic approximations of a highly nonlinear problem to arbitrarily high order by means of the HAM in only a few seconds. Inspired by the recent successful applications of the HAM in different fields, a Mathematica package based on the HAM, called BVPh, has been made available online for solving nonlinear boundary-value problems . BVPh is a solver package for highly nonlinear ODEs with singularities, multiple solutions, and multipoint boundary conditions in either a finite or an infinite interval, and includes support for certain types of nonlinear PDEs. [8] Another HAM-based Mathematica code, APOh, has been produced to solve for an explicit analytic approximation of the optimal exercise boundary of American put option, which is also available online .

Frequency response analysis for nonlinear oscillators

The HAM has recently been reported to be useful for obtaining analytical solutions for nonlinear frequency response equations. Such solutions are able to capture various nonlinear behaviors such as hardening-type, softening-type or mixed behaviors of the oscillator. [19] [20] These analytical equations are also useful in prediction of chaos in nonlinear systems. [21]

Related Research Articles

In computational mathematics, an iterative method is a mathematical procedure that uses an initial value to generate a sequence of improving approximate solutions for a class of problems, in which the n-th approximation is derived from the previous ones.

<span class="mw-page-title-main">Partial differential equation</span> Type of differential equation

In mathematics, a partial differential equation (PDE) is an equation which computes a function between various partial derivatives of a multivariable function.

<span class="mw-page-title-main">Numerical methods for ordinary differential equations</span> Methods used to find numerical solutions of ordinary differential equations

Numerical methods for ordinary differential equations are methods used to find numerical approximations to the solutions of ordinary differential equations (ODEs). Their use is also known as "numerical integration", although this term can also refer to the computation of integrals.

<span class="mw-page-title-main">Optimal control</span> Mathematical way of attaining a desired output from a dynamic system

Optimal control theory is a branch of control theory that deals with finding a control for a dynamical system over a period of time such that an objective function is optimized. It has numerous applications in science, engineering and operations research. For example, the dynamical system might be a spacecraft with controls corresponding to rocket thrusters, and the objective might be to reach the Moon with minimum fuel expenditure. Or the dynamical system could be a nation's economy, with the objective to minimize unemployment; the controls in this case could be fiscal and monetary policy. A dynamical system may also be introduced to embed operations research problems within the framework of optimal control theory.

In mathematics, integral equations are equations in which an unknown function appears under an integral sign. In mathematical notation, integral equations may thus be expressed as being of the form:

In mathematics and its applications, a Sturm–Liouville problem is a second-order linear ordinary differential equation of the form:

The Adomian decomposition method (ADM) is a semi-analytical method for solving ordinary and partial nonlinear differential equations. The method was developed from the 1970s to the 1990s by George Adomian, chair of the Center for Applied Mathematics at the University of Georgia. It is further extensible to stochastic systems by using the Ito integral. The aim of this method is towards a unified theory for the solution of partial differential equations (PDE); an aim which has been superseded by the more general theory of the homotopy analysis method. The crucial aspect of the method is employment of the "Adomian polynomials" which allow for solution convergence of the nonlinear portion of the equation, without simply linearizing the system. These polynomials mathematically generalize to a Maclaurin series about an arbitrary external parameter; which gives the solution method more flexibility than direct Taylor series expansion.

<span class="mw-page-title-main">Duffing equation</span> Non-linear second order differential equation and its attractor

The Duffing equation, named after Georg Duffing (1861–1944), is a non-linear second-order differential equation used to model certain damped and driven oscillators. The equation is given by

In mathematics, and more specifically in partial differential equations, Duhamel's principle is a general method for obtaining solutions to inhomogeneous linear evolution equations like the heat equation, wave equation, and vibrating plate equation. It is named after Jean-Marie Duhamel who first applied the principle to the inhomogeneous heat equation that models, for instance, the distribution of heat in a thin plate which is heated from beneath. For linear evolution equations without spatial dependency, such as a harmonic oscillator, Duhamel's principle reduces to the method of variation of parameters technique for solving linear inhomogeneous ordinary differential equations. It is also an indispensable tool in the study of nonlinear partial differential equations such as the Navier–Stokes equations and nonlinear Schrödinger equation where one treats the nonlinearity as an inhomogeneity.

The theory of optimal control is concerned with operating a dynamic system at minimum cost. The case where the system dynamics are described by a set of linear differential equations and the cost is described by a quadratic function is called the LQ problem. One of the main results in the theory is that the solution is provided by the linear–quadratic regulator (LQR), a feedback controller whose equations are given below.

Numerical continuation is a method of computing approximate solutions of a system of parameterized nonlinear equations,

In numerical linear algebra, the alternating-direction implicit (ADI) method is an iterative method used to solve Sylvester matrix equations. It is a popular method for solving the large matrix equations that arise in systems theory and control, and can be formulated to construct solutions in a memory-efficient, factored form. It is also used to numerically solve parabolic and elliptic partial differential equations, and is a classic method used for modeling heat conduction and solving the diffusion equation in two or more dimensions. It is an example of an operator splitting method.

In mathematics, inertial manifolds are concerned with the long term behavior of the solutions of dissipative dynamical systems. Inertial manifolds are finite-dimensional, smooth, invariant manifolds that contain the global attractor and attract all solutions exponentially quickly. Since an inertial manifold is finite-dimensional even if the original system is infinite-dimensional, and because most of the dynamics for the system takes place on the inertial manifold, studying the dynamics on an inertial manifold produces a considerable simplification in the study of the dynamics of the original system.

Exponential integrators are a class of numerical methods for the solution of ordinary differential equations, specifically initial value problems. This large class of methods from numerical analysis is based on the exact integration of the linear part of the initial value problem. Because the linear part is integrated exactly, this can help to mitigate the stiffness of a differential equation. Exponential integrators can be constructed to be explicit or implicit for numerical ordinary differential equations or serve as the time integrator for numerical partial differential equations.

<span class="mw-page-title-main">Parareal</span> Parallel algorithm from numerical analysis

Parareal is a parallel algorithm from numerical analysis and used for the solution of initial value problems. It was introduced in 2001 by Lions, Maday and Turinici. Since then, it has become one of the most widely studied parallel-in-time integration methods.

In mathematics, an abstract differential equation is a differential equation in which the unknown function and its derivatives take values in some generic abstract space. Equations of this kind arise e.g. in the study of partial differential equations: if to one of the variables is given a privileged position and all the others are put together, an ordinary "differential" equation with respect to the variable which was put in evidence is obtained. Adding boundary conditions can often be translated in terms of considering solutions in some convenient function spaces.

In solid mechanics, the linear stability analysis of an elastic solution is studied using the method of incremental deformations superposed on finite deformations. The method of incremental deformation can be used to solve static, quasi-static and time-dependent problems. The governing equations of the motion are ones of the classical mechanics, such as the conservation of mass and the balance of linear and angular momentum, which provide the equilibrium configuration of the material. The main corresponding mathematical framework is described in the main Raymond Ogden's book Non-linear elastic deformations and in Biot's book Mechanics of incremental deformations, which is a collection of his main papers.

The variational multiscale method (VMS) is a technique used for deriving models and numerical methods for multiscale phenomena. The VMS framework has been mainly applied to design stabilized finite element methods in which stability of the standard Galerkin method is not ensured both in terms of singular perturbation and of compatibility conditions with the finite element spaces.

In mathematics, Anderson acceleration, also called Anderson mixing, is a method for the acceleration of the convergence rate of fixed-point iterations. Introduced by Donald G. Anderson, this technique can be used to find the solution to fixed point equations often arising in the field of computational science.

Nonlinear tides are generated by hydrodynamic distortions of tides. A tidal wave is said to be nonlinear when its shape deviates from a pure sinusoidal wave. In mathematical terms, the wave owes its nonlinearity due to the nonlinear advection and frictional terms in the governing equations. These become more important in shallow-water regions such as in estuaries. Nonlinear tides are studied in the fields of coastal morphodynamics, coastal engineering and physical oceanography. The nonlinearity of tides has important implications for the transport of sediment.

References

  1. Liao, S.J. (1992), The proposed homotopy analysis technique for the solution of nonlinear problems, PhD thesis, Shanghai Jiao Tong University
  2. Liao, S.J. (1999), "An explicit, totally analytic approximation of Blasius' viscous flow problems", International Journal of Non-Linear Mechanics, 34 (4): 759–778, Bibcode:1999IJNLM..34..759L, doi:10.1016/S0020-7462(98)00056-0
  3. Liao, S.J. (2003), Beyond Perturbation: Introduction to the Homotopy Analysis Method, Boca Raton: Chapman & Hall/ CRC Press, ISBN   978-1-58488-407-1
  4. Adomian, G. (1994). Solving Frontier problems of Physics: The decomposition method. Kluwer Academic Publishers.
  5. Liang, Songxin; Jeffrey, David J. (2009), "Comparison of homotopy analysis method and homotopy perturbation method through an evolution equation", Communications in Nonlinear Science and Numerical Simulation, 14 (12): 4057–4064, Bibcode:2009CNSNS..14.4057L, doi:10.1016/j.cnsns.2009.02.016
  6. Sajid, M.; Hayat, T. (2008), "Comparison of HAM and HPM methods in nonlinear heat conduction and convection equations", Nonlinear Analysis: Real World Applications, 9 (5): 2296–2301, doi:10.1016/j.nonrwa.2007.08.007
  7. Motsa, S.S.; Sibanda, P.; Awad, F.G.; Shateyi, S. (2010), "A new spectral-homotopy analysis method for the MHD Jeffery–Hamel problem", Computers & Fluids, 39 (7): 1219–1225, doi:10.1016/j.compfluid.2010.03.004
  8. 1 2 Liao, S.J. (2012), Homotopy Analysis Method in Nonlinear Differential Equations, Berlin & Beijing: Springer & Higher Education Press, ISBN   978-7-04-032298-9
  9. Vajravelu, K.; Van Gorder (2013), Nonlinear Flow Phenomena and Homotopy Analysis, Berlin & Beijing: Springer & Higher Education Press, ISBN   978-3-642-32102-3
  10. Xu, D.L.; Lin, Z.L.; Liao, S.J.; Stiassnie, M. (2012), "On the steady-state fully resonant progressive waves in water of finite depth", Journal of Fluid Mechanics, 710: 379–418, Bibcode:2012JFM...710..379X, doi:10.1017/jfm.2012.370, S2CID   122094345
  11. Liao, S.J. (2013), "Do peaked solitary water waves indeed exist?", Communications in Nonlinear Science and Numerical Simulation, 19 (6): 1792–1821, arXiv: 1204.3354 , Bibcode:2014CNSNS..19.1792L, doi:10.1016/j.cnsns.2013.09.042, S2CID   119203215
  12. Abbasbandy, S. (2006), "The application of homotopy analysis method to nonlinear equations arising in heat transfer", Physics Letters A, 360 (1): 109–113, Bibcode:2006PhLA..360..109A, doi:10.1016/j.physleta.2006.07.065
  13. Chen, Y.M.; Liu, J.K. (2009), "Uniformly valid solution of limit cycle of the Duffing–van der Pol equation", Mechanics Research Communications, 36 (7): 845–850, doi:10.1016/j.mechrescom.2009.06.001
  14. Zhu, S.P. (2006), "An exact and explicit solution for the valuation of American put options", Quantitative Finance, 6 (3): 229–242, doi:10.1080/14697680600699811, S2CID   121851109
  15. Turkyilmazoglu, M. (2009), "Purely analytic solutions of the compressible boundary layer flow due to a porous rotating disk with heat transfer", Physics of Fluids, 21 (10): 106104–106104–12, Bibcode:2009PhFl...21j6104T, doi:10.1063/1.3249752
  16. Park, Sang-Hyeon; Kim, Jeong-Hoon (2011), "Homotopy analysis method for option pricing under stochastic volatility", Applied Mathematics Letters, 24 (10): 1740–1744, doi: 10.1016/j.aml.2011.04.034
  17. Mastroberardino, A. (2011), "Homotopy analysis method applied to electrohydrodynamic flow", Commun. Nonlinear. Sci. Numer. Simulat., 16 (7): 2730–2736, Bibcode:2011CNSNS..16.2730M, doi:10.1016/j.cnsns.2010.10.004
  18. Nassar, Christopher J.; Revelli, Joseph F.; Bowman, Robert J. (2011), "Application of the homotopy analysis method to the Poisson–Boltzmann equation for semiconductor devices", Commun Nonlinear Sci Numer Simulat, 16 (6): 2501–2512, Bibcode:2011CNSNS..16.2501N, doi:10.1016/j.cnsns.2010.09.015
  19. Tajaddodianfar, Farid (2017). "Nonlinear dynamics of MEMS/NEMS resonators: analytical solution by the homotopy analysis method". Microsystem Technologies. 23 (6): 1913–1926. doi:10.1007/s00542-016-2947-7. S2CID   113216381.
  20. Tajaddodianfar, Farid (March 2015). "On the dynamics of bistable micro/nano resonators: Analytical solution and nonlinear behavior". Communications in Nonlinear Science and Numerical Simulation. 20 (3): 1078–1089. Bibcode:2015CNSNS..20.1078T. doi:10.1016/j.cnsns.2014.06.048.
  21. Tajaddodianfar, Farid (January 2016). "Prediction of chaos in electrostatically actuated arch micro-nano resonators: Analytical approach". Communications in Nonlinear Science and Numerical Simulation. 30 (1–3): 182–195. doi: 10.1016/j.cnsns.2015.06.013 .