At the intersection of Numerical Analysis and Spectral Geometry

TL;DR

This survey explores how geometric properties influence spectral computations and discusses advanced numerical methods for accurate eigenvalue approximation, emphasizing their roles in spectral geometry research and proofs.

Contribution

It provides a comprehensive overview of discretization techniques, error control, and the interplay between numerical analysis and spectral geometry, highlighting recent methodological advances.

Findings

Validated computations aid in spectral geometry conjectures

High-accuracy discretizations improve eigenvalue approximations

Numerical methods are integral to proof strategies in spectral geometry

Abstract

How do the geometric properties of a domain impact the spectrum of an operator defined on it? How do we compute accurate and reliable approximations of these spectra? The former question is studied in spectral geometry, and the latter is a central concern in numerical analysis. In this short expository survey we revisit the process of eigenvalue approximation, from the perspective of computational spectral geometry. Over the years a multitude of methods -- for discretizing the operator and for the resultant discrete system -- have been developed and analyzed in the field of numerical analysis. High-accuracy and provably convergent discretization approaches can be used to examine the interplay between the spectrum of an operator and the geometric properties of the spatial domain or manifold it is defined on. While computations have been used to guide conjectures in spectral geometry, in recent years approximation-theoretic tools and validated computations are also being used as part of proof strategies in spectral geometry. Given a particular spectral feature of interest, should we discretize the original problem, or seek a reformulation? Of the many possible approximation strategies, which should we choose? These choices are inextricably linked to the objective: on the one hand, rapid, specialized methods are often ideal for conjecture formulation (prioritizing efficiency and accuracy), whereas schemes with guaranteed, computable error bounds are needed when computation is incorporated into a proof strategy. We also review instances where the demanding requirements of spectral geometry -- the need for rigorous error control or the robust calculation of higher eigenvalues -- motivate new developments in numerical analysis.