Geodesic curves in Gaussian random field manifolds
Alexandre L. M. Levada

TL;DR
This paper introduces a numerical method to compute geodesic distances in Gaussian random field manifolds, revealing potential irreversible geometric deformations in their trajectories.
Contribution
It derives the metric tensor and Christoffel symbols for Gaussian random field manifolds and develops a Runge-Kutta based numerical approach to estimate geodesic distances.
Findings
The method accurately estimates geodesic distances under various conditions.
Reversing geodesic equations often yields different trajectories, indicating irreversibility.
The approach enhances understanding of the geometric structure of random field models.
Abstract
Random fields are mathematical structures used to model the spatial interaction of random variables along time, with applications ranging from statistical physics and thermodynamics to system's biology and the simulation of complex systems. Despite being studied since the 19th century, little is known about how the dynamics of random fields are related to the geometric properties of their parametric spaces. For example, how can we quantify the similarity between two random fields operating in different regimes using an intrinsic measure? In this paper, we propose a numerical method for the computation of geodesic distances in Gaussian random field manifolds. First, we derive the metric tensor of the underlying parametric space (the 3 x 3 first-order Fisher information matrix), then we derive the 27 Christoffel symbols required in the definition of the system of non-linear differential…
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Taxonomy
TopicsScientific Research and Discoveries · Gaussian Processes and Bayesian Inference · Plant Water Relations and Carbon Dynamics
