Georgia Institute of Technology College of Sciences

Two quantitative notions of mixing are the decay of correlations and the decay of a mix-norm --- a negative Sobolev norm --- and the intensity of mixing can be measured by the rates of decay of these quantities. From duality, correlations are uniformly dominated by a mix-norm; but can they decay asymptotically faster than the mix-norm? We answer this question by constructing an observable with correlation that comes arbitrarily close to achieving the decay rate of the mix-norm. Therefore the mix-norm is the sharpest rate of decay of correlations in both the uniform sense and the asymptotic sense. Moreover, there exists an observable with correlation that decays at the same rate as the mix-norm if and only if the rate of decay of the mix-norm is achieved by its projection onto low-frequency Fourier modes. In this case, the function being mixed is called q-recurrent; otherwise it is q-transient. We use this classification to study several examples and raise questions for future investigations. 

We will prove lower bounds for energy functionals of mappings of the real, complex and quaternionic projective spaces with their canonical Riemannian metrics.  For real and complex projective spaces, these results are sharp, and we will characterize the family of energy-minimizing mappings which occur in these results.  For complex projective spaces, these results extend to all Kahler metrics.  We will discuss the connections between these results and several theorems and questions in systolic geometry.

Wild-type zebrafish are named for their dark and light stripes, but mutant zebrafish feature variable skin patterns, including spots and labyrinth curves. All of these patterns form as the fish grow due to the interactions of tens of thousands of pigment cells in the skin. This leads to the question: how do cell interactions change to create mutant patterns? The longterm biological motivation for my work is to shed light on this question — I strive to help link genes, cell behavior, and visible animal characteristics. Toward this goal, I build agent-based models to describe cell behavior in growing fish body and fin-shaped domains. However, my models are stochastic and have many parameters, and comparing simulated patterns, alternative models, and fish images is often a qualitative process. This, in turn, drives my mathematical goal: I am interested in developing methods for quantifying variable cell-based patterns and linking computational and analytically tractable models. In this talk, I will overview our agent-based models for body and fin pattern formation, share how topological data analysis can be used to quantify cell-based patterns and models, and discuss ongoing work on relating agent-based and continuum models for zebrafish patterns.