Transport-Generated Signals Uncover Geometric Features of Evolving Branched Structures

TL;DR

This paper introduces a non-invasive, scalable method to infer the geometric features of evolving branched structures by analyzing signals from tracer particles, enabling insights into complex systems without internal measurements.

Contribution

The authors develop a general framework that uses transport-generated signals to recover geometric features of dynamic branched structures without needing internal data.

Findings

Signal intensity statistics encode network extent and trapping frequency.

Method infers structural features from external observations.

Applicable to diverse natural and engineered systems.

Abstract

Branched structures that evolve over time critically determine the function of various natural and engineered systems, including growing vasculature, neural arborization, pulmonary networks such as lungs, river basins, power distribution networks, and synthetic flow media. Inferring the underlying geometric properties of such systems and monitoring their structural and morphological evolution is therefore essential. However, this remains a major challenge due to limited access and the transient nature of the internal states. Here, we present a general framework for recovering the geometric features of evolving branched structures by analyzing the signals generated by tracer particles during transport. As tracers traverse the structure, they emit detectable pulses upon reaching a fixed observation point. We show that the statistical properties of this signal intensity -- which reflect underlying first-passage dynamics -- encode key structural features such as network extent, localized trapping frequency, and bias of motion (e.g., due to branch tapering). Crucially, this method enables inference from externally observable quantities, requiring no knowledge of individual particle trajectories or internal measurements. Our approach provides a scalable, non-invasive strategy for probing dynamic complex geometries across a wide range of systems.