Dissertation: Geodesics of Random Riemannian Metrics
Tom LaGatta

TL;DR
This paper introduces Riemannian First-Passage Percolation, a new model of random differential geometry, proving a shape theorem and showing that geodesics are almost surely not globally minimizing, revealing novel properties of random Riemannian metrics.
Contribution
It develops the Riemannian FPP model, proves a shape theorem, and demonstrates that geodesics are almost surely not globally minimizing, a novel insight in continuum random geometry.
Findings
Large balls converge to a deterministic shape under rescaling.
Smooth random Riemannian metrics are geodesically complete with probability one.
Geodesics starting from a fixed point in any direction are almost surely not globally minimizing.
Abstract
We introduce Riemannian First-Passage Percolation (Riemannian FPP) as a new model of random differential geometry, by considering a random, smooth Riemannian metric on . We are motivated in our study by the random geometry of first-passage percolation (FPP), a lattice model which was developed to model fluid flow through porous media. By adapting techniques from standard FPP, we prove a shape theorem for our model, which says that large balls under this metric converge to a deterministic shape under rescaling. As a consequence, we show that smooth random Riemannian metrics are geodesically complete with probability one. In differential geometry, geodesics are curves which locally minimize length. They need not do so globally: consider great circles on a sphere. For lattice models of FPP, there are many open questions related to minimizing geodesics; similarly, it is…
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Taxonomy
TopicsStochastic processes and statistical mechanics · Geometry and complex manifolds · Markov Chains and Monte Carlo Methods
