Kevin Zumbrun

Mathew A. Johnson, Kevin Zumbrun

By the introduction of a generalized Evans function defined by an appropriate 2-modified Fredholm determinant, we give a simple proof of convergence in location and multiplicity of Hill's method for numerical approximation of spectra of periodic-coefficient ordinary differential operators. Our results apply to operators of nondegenerate type, under the condition that the principal coefficient matrix be symmetric positive definite (automatically satisfied in the scalar case). Notably, this includes a large class of nonselfadjoint operators, which were previously not treated. The case of general coefficients depends on an interesting operator-theoretic question regarding properties of Toeplitz matrices.

Kevin Zumbrun

We present a family of numerical implementations of Kato's ODE propagating global bases of analytically varying invariant subspaces, of which the first-order version is a surprising simple "greedy algorithm" that is both stable and easy to program and the second-order version a relaxation of a first-order scheme of Brin and Zumbrun. The method has application to numerical Evans function computations used to assess stability of traveling-wave solutions of time-evolutionary PDE.

Shantia Yarahmadian, Blake Barker, Kevin Zumbrun et al.

We generalize the Dogterom-Leibler model for microtubule dynamics [DL] to the case where the rates of elongation as well as the lifetimes of the elongating and shortening phases are a function of GTP-tubulin concentration. We study also the effect of nucleation rate in the form of a damping term which leads to new steady-states. For this model, we study existence and stability of steady states satisfying the boundary conditions at x = 0. Our stability analysis introduces numerical and analytical Evans function computations as a new mathematical tool in the study of microtubule dynamics.

Jeffrey Humpherys, Kevin Zumbrun

As described in the classic works of Lee--Stewart and Short--Stewart, the numerical evaluation of linear stability of planar detonation waves is a computationally intensive problem of considerable interest in applications. Reexamining this problem from a modern numerical Evans function point of view, we derive a new algorithm for their stability analysis, related to a much older method of Erpenbeck, that, while equally simple and easy to implement as the standard method introduced by Lee--Stewart, appears to be potentially faster and more stable.

Blake Barker, Kevin zumbrun

We carry out a systematic numerical stability analysis of ZND detonations of Majda's model with Arrhenius-type ignition function, a simplified model for reacting flow, as heat release and activation energy are varied. Our purpose is, first, to answer a question of Majda whether oscillatory instabilities can occur for high activation energies as in the full reacting Euler equations, and, second, to test the efficiency of various versions of a numerical eigenvalue-finding scheme suggested by Humpherys and Zumbrun against the standard method of Lee and Stewart. Our results suggest that instabilities do not occur for Majda's model with Arrhenius-type ignition function, nor with a modified Arrhenius-type ignition function suggested by Lyng--Zumbrun, even in the high-activation energy limit. We find that the algorithm of Humpherys--Zumbrun is in the context of Majda's model $100$-$1,000$ times faster than the one described in the classical work of Lee and Stewart and $1$-$10$ times faster than an optimized version of the Lee--Stewart algorithm using an adaptive-mesh ODE solver

Kevin Zumbrun

We perform error analyses explaining some previously mysterious phenomena arising in numerical computation of the Evans function, in particular (i) the advantage of centered coordinates for exterior product and related methods, and (ii) the unexpected stability of the (notoriously unstable) continuous orthogonalization method of Drury in the context of Evans function applications. The analysis in both cases centers around a numerical version of the gap lemma of Gardner--Zumbrun and Kapitula--Sandstede, giving uniform error estimates for apparently ill-posed projective boundary-value problems with asymptotically constant coefficients, so long as the rate of convergence of coefficients is greater than the "badness" of the boundary projections as measured by negative spectral gap. In the second case, we use also the simple but apparently previously unremarked observation that the Drury method is in fact (neutrally) stable when used to approximate an unstable subspace, so that continuous orthogonalization and the centered exterior product method are roughly equally well-conditioned as methods for Evans function approximation. The latter observation makes possible an extremely simple nonlinear boundary-value method for possible use in large-scale systems, extending ideas suggested by Sandstede. We suggest also a related linear method based on the conjugation lemma of Métivier--Zumbrun, an extension of the gap lemma mentioned above.

Jeffrey Humpherys, Kevin Zumbrun

In Evans function computations of the spectra of asymptotically constant-coefficient linear operators, a basic issue is the efficient and numerically stable computation of subspaces evolving according to the associated eigenvalue ODE. For small systems, a fast, shooting algorithm may be obtained by representing subspaces as single exterior products \cite{AS,Br.1,Br.2,BrZ,BDG}. For large systems, however, the dimension of the exterior-product space quickly becomes prohibitive, growing as $\binom{n}{k}$, where $n$ is the dimension of the system written as a first-order ODE and $k$ (typically $\sim n/2$) is the dimension of the subspace. We resolve this difficulty by the introduction of a simple polar coordinate algorithm representing ``pure'' (monomial) products as scalar multiples of orthonormal bases, for which the angular equation is a numerically optimized version of the continuous orthogonalization method of Drury--Davey \cite{Da,Dr} and the radial equation is evaluable by quadrature. Notably, the polar-coordinate method preserves the important property of analyticity with respect to parameters.