An inverse problem for renormalized area: determining the bulk metric with minimal surfaces

We present an inverse problem which uses the renormalized area functional on minimal submanifolds to determine the asymptotic expansion of asymptotically hyperbolic, conformally compact metrics which are partially even to high order. We use a rigidity argument to determine the conformal infinity of the metric via the renormalized area. We then consider the renormalized volume of perturbations of the hemisphere to determine the higher order terms in the asymptotic expansion of the metric. We prove global rigidity when these metrics are log-analytic, and further note that renormalized area determines the obstruction tensor for PE metrics.

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Publication Details

Journal
New Zealand journal of mathematics
Published
2025-09-10
DOI
https://doi.org/10.53733/552
Primary Topic
Numerical methods in inverse problems
Type
article
Field-Weighted Citation Impact
0.00

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article

An inverse problem for renormalized area: determining the bulk metric with minimal surfaces

Jared Marx-Kuo
New Zealand journal of mathematics
Numerical methods in inverse problems
article

An inverse problem for renormalized area: determining the bulk metric with minimal surfaces

Jared Marx-Kuo
article en

Abstract

We present an inverse problem which uses the renormalized area functional on minimal submanifolds to determine the asymptotic expansion of asymptotically hyperbolic, conformally compact metrics which are partially even to high order. We use a rigidity argument to determine the conformal infinity of the metric via the renormalized area. We then consider the renormalized volume of perturbations of the hemisphere to determine the higher order terms in the asymptotic expansion of the metric. We prove global rigidity when these metrics are log-analytic, and further note that renormalized area determines the obstruction tensor for PE metrics.

New Zealand journal of mathematicsVol. 56
Rice University (US)
National Science Foundation
Openalex Percentile: Top 70%
Numerical methods in inverse problems
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