Georgia Institute of Technology College of Sciences

Dehn surgery (with a fixed slope p/q) associates, to a knot in S^3, a 3-manifold M with first homology isomorphic to the integers mod p. One might wonder if this function is one-to-one or onto; Cameron Gordon (1978) conjectured that it is never injective nor surjective. The surjectivity case was established a decade later, while the injectivity case was only recently proven by Hayden, Piccirillo, and Wakelin. We will survey this latter result and its proof. 

An $S_k$-set is a subset of a group whose $k$-tuples have distinct products. We give explicit constructions of large $S_k$-sets in the groups $\mathrm{Sym}(n)$ and $\mathrm{Alt}(n)$ and of large $S_2$-sets in $\mathrm{Sym}(n)\times\mathrm{Sym}(n)$ and $\mathrm{Alt}(n)\times\mathrm{Alt}(n)$, as well as some probabilistic constructions for 'nice' groups. We show that $k$ is even and $\Gamma$ has a normal abelian subgroup with bounded index then any $S_k$-set has size at most $(1-\varepsilon)|\Gamma|^{1/k}$. We describe some connections between $S_k$-sets and extremal graph theory. We determine up to a constant factor the largest possible minimum outdegree in a digraph with no subgraph in $\{C_{2,2},\ldots,C_{k,k}\}$, where $C_{\ell,\ell}$ is the orientation of $C_{2\ell}$ with two maximal directed $\ell$-paths. Contrasting with undirected cycles, the extremal minimum outdegree for $\{C_{2,2},\ldots,C_{k,k}\}$ is much smaller than that for any $C_{\ell,\ell}$. We count the directed Hamilton cycles in one of our constructions to improve the upper bound for a problem on Hamilton paths introduced by Cohen, Fachini, and Körner. Joint work with Michael Tait.

This talk presents two recent advances in Neural Network with Local Converging Inputs (NNLCI) —a novel surrogate model for efficiently resolving nonlinear flow dynamics at modest computational cost

First, a powerful and efficient technique is introduced to extend NNLCI to unstructured computational fluid dynamics. The framework is validated on two-dimensional inviscid supersonic flow in channels with varying bump geometries and positions. The NNLCI model accurately captures key flowfield structures and dynamics, including regions with highly nonlinear shock interactions while achieving a speedup of more than two orders of magnitude.

Second, we conduct a comprehensive benchmark study to compare our method with current state-of-the-art AI-based PDE solvers. Across representative hyperbolic conservation law problems, NNLCI consistently deliver superior accuracy, efficiency and robustness in resolving challenging sharp discontinuities and wave interactions. The work provides practical guidance for model selection in scientific machine learning applications

Proving conjectures is an essential part of our job as mathematicians. Another essential part is to come up with plausible conjectures. In the talk, we focus on this part. We present a new twist to an old method from computer algebra for detecting recurrence equations of infinite sequences of which only the first few terms are known. By applying this new version systematically to all the entries of the Online Encyclopedia of Integer Sequences, we detected a number of potential recurrence equations that could not be found by the classical methods. Some of these have meanwhile been proven. This is joint work with Christoph Koutschan. 

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Titile: Lattice Reduction 
                                                                                                           
Abstract: It is well known how to go from an exact number (e.g. 1/3) into an approximation (e.g. 0.333). But how can we get back? At first glance, this seems impossible, because some information got lost during the approximation. However, there are techniques for doing this and similar seemingly magic tricks. We will discuss some such tricks that rely on an algorithm for finding short vectors in integer lattices.