Comparable upper and lower bounds for boundary values of Neumann eigenfunctions and tight inclusion of eigenvalues

TL;DR

This paper establishes tight boundary bounds for Neumann eigenfunctions on smooth domains, introduces an improved eigenvalue inclusion bound, and enhances numerical eigenpair computations with increased accuracy.

Contribution

It provides the first tight boundary bounds for Neumann eigenfunctions, an improved eigenvalue inclusion bound, and an enhanced numerical method for computing Neumann eigenpairs.

Findings

Boundary bounds are tight and independent of eigenvalue.

The new inclusion bound improves accuracy by a factor of E^{5/6}.

Numerical eigenvalue computation accuracy increased from 9 to 14 digits.

Abstract

For smooth bounded domains in $\mathbb{R}$, we prove upper and lower $L^2$ bounds on the boundary data of Neumann eigenfunctions, and prove quasi-orthogonality of this boundary data in a spectral window. The bounds are tight in the sense that both are independent of eigenvalue; this is achieved by working with an appropriate norm for boundary functions, which includes a `spectral weight', that is, a function of the boundary Laplacian. This spectral weight is chosen to cancel concentration at the boundary that can happen for `whispering gallery' type eigenfunctions. These bounds are closely related to wave equation estimates due to Tataru. Using this, we bound the distance from an arbitrary Helmholtz parameter $E>0$ to the nearest Neumann eigenvalue, in terms of boundary normal-derivative data of a trial function $u$ solving the Helmholtz equation $(\Delta-E)u=0$. This `inclusion bound' improves over previously known bounds by a factor of $E^{5/6}$. It is analogous to a recently improved inclusion bound in the Dirichlet case, due to the first two authors. Finally, we apply our theory to present an improved numerical implementation of the method of particular solutions for computation of Neumann eigenpairs on smooth planar domains. We show that the new inclusion bound improves the relative accuracy in a computed Neumann eigenvalue (around the $42000$th) from 9 digits to 14 digits, with little extra effort.