On the Identifiablility of Nonlocal Interaction Kernels in First-Order Systems of Interacting Particles on Riemannian Manifolds

TL;DR

This paper investigates the theoretical identifiability of interaction kernels in particle systems on Riemannian manifolds, establishing conditions for stable recovery from data and analyzing the inverse problem's well-posedness.

Contribution

It introduces a linear inverse problem framework for identifying interaction functions, proves its well-posedness on certain manifolds, and discusses implications for statistical estimation and regularization.

Findings

Stable recovery of interaction kernels is possible from finite noisy data.

The inverse problem is well-posed on spheres and hyperbolic spaces.

Least squares estimators can be statistically optimal under certain conditions.

Abstract

In this paper, we tackle a critical issue in nonparametric inference for systems of interacting particles on Riemannian manifolds: the identifiability of the interaction functions. Specifically, we define the function spaces on which the interaction kernels can be identified given infinite i.i.d observational derivative data sampled from a distribution. Our methodology involves casting the learning problem as a linear statistical inverse problem using a operator theoretical framework. We prove the well-posedness of inverse problem by establishing the strict positivity of a related integral operator and our analysis allows us to refine the results on specific manifolds such as the sphere and Hyperbolic space. Our findings indicate that a numerically stable procedure exists to recover the interaction kernel from finite (noisy) data, and the estimator will be convergent to the ground truth. This also answers an open question in [MMQZ21] and demonstrate that least square estimators can be statistically optimal in certain scenarios. Finally, our theoretical analysis could be extended to the mean-field case, revealing that the corresponding nonparametric inverse problem is ill-posed in general and necessitates effective regularization techniques.