Reconstruction and interpolation of manifolds II: Inverse problems with partial data for distances observations and for the heat kernel

TL;DR

This paper develops methods for reconstructing Riemannian manifolds from partial distance data and noisy heat kernel observations, with applications to manifold learning and inverse problems.

Contribution

It extends previous work by addressing partial data scenarios and inverse problems involving the heat kernel with disjoint source and observation sets.

Findings

Stable reconstruction of manifolds from partial noisy distance data.

Uniqueness results for inverse heat kernel problems without noise.

Application frameworks for manifold learning with limited data.

Abstract

We consider how a closed Riemannian manifold $M$ and its metric tensor $g$ can be approximately reconstructed from local distance measurements. Moreover, we consider an inverse problem of determining $(M,g)$ from limited knowledge on the heat kernel. In the part 1 of the paper, we considered the approximate construction of a smooth manifold in the case when one is given the noisy distances $\tilde d(x,y)=d(x,y)+\varepsilon_{x,y}$ for all points $x,y\in X$, where $X$ is a $\delta$-dense subset of $M$ and $|\varepsilon_{x,y}|<\delta$. In this part 2 of the paper, we consider a similar problem with partial data, that is, the approximate construction of the manifold $(M,g)$ when we are given $\tilde d(x,y)$ for $x\in X$ and $y \in U\cap X$, where $U$ is an open subset of $M$. In addition, we consider the inverse problem of determining the manifold $(M,g)$ with non-negative Ricci curvature from noisy observations of the heat kernel $G(y,z,t)$. We show that a manifold approximating $(M,g)$ can be determined in a stable way, when for some unknown source points $z_j$ in $X\setminus U$, we are given the values of the heat kernel $G(y,z_k,t)$ for $y\in X\cap U$ and $t\in (0,1)$ with a multiplicative noise. We also give a uniqueness result for the inverse problem in the case when the data does not contain noise and consider applications in manifold learning. A novel feature of the inverse problem for the heat kernel is that the set $M\setminus U$ containing the sources and the observation set $U$ are disjoint.