1991
DOI: 10.1142/s0218127491000555
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Numerical Analysis and Control of Bifurcation Problems (Ii): Bifurcation in Infinite Dimensions

Abstract: A number of basic algorithms for the numerical analysis and control of bifurcation phenomena are described. The emphasis is on algorithms based on pseudoarclength continuation for ordinary differential equations. Several illustrative examples computed with the AUTO software package are included. This is Part II of the paper that appeared in the preceding issue [Doedel et al., 1991] and that mainly dealt with algebraic problems.

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Cited by 378 publications
(226 citation statements)
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“…given mean thicknesses we use continuation techniques 67,68,69 . We start with analytically known stationary periodic small-amplitude profiles, which correspond to the linear eigenfunctions for the critical wave number k c .…”
Section: Non-linear Behaviour a Stationary Solutions As Extrema mentioning
confidence: 99%
“…given mean thicknesses we use continuation techniques 67,68,69 . We start with analytically known stationary periodic small-amplitude profiles, which correspond to the linear eigenfunctions for the critical wave number k c .…”
Section: Non-linear Behaviour a Stationary Solutions As Extrema mentioning
confidence: 99%
“…Factoring out the group invariance (through so-called pinning conditions) is also an important component of many numerical methods for systems with symmetry. For instance, a similar templatebased phase condition is often used in the computation of periodic solutions of autonomous ordinary differential equations (ODEs) [4], and a related method was recently used in [19] for bifurcation analysis of systems where the governing equations are not explicitly known, but only a numerical timestepper is available.…”
Section: Introductionmentioning
confidence: 99%
“…General methods [22][23][24][25] for the solution of nonlinear two-point boundary value problems tend to be geared toward ordinary differential equations, and can be prohibitively expensive for partial differential equations. Recently [26][27][28], Wilkening and Ambrose introduced an efficient method of computing timeperiodic solutions of nonlinear PDEs.…”
Section: The Adjoint Continuation Methods (Acm)mentioning
confidence: 99%
“…In general terms, the ACM operates by treating the task of finding the initial condition of a periodic orbit as an unconstrained minimization problem. One advantage of this approach over, say, the orthogonal collocation method implemented in AUTO [22,23] is that there are many fewer degrees of freedom to compute (as only the initial conditions are unknown). For example, in a typical simulation, we use 1024 grid points in space and 500 timesteps, each broken into 8 Runge-Kutta stages.…”
Section: Appendix a Acm Implementation Flowchartmentioning
confidence: 99%