Janitha Awedige will discuss the paper

“Inference Rules for Probability Logic” by Marija Boricic.

Topological data analysis (TDA) is a new approach to analyzing complexdata which often helps reveal otherwise hidden patterns by highlightingvarious geometrical and topological features of the data. Persistenthomology is a key in the TDA toolbox. It measures topological featuresof data that persist across multiple scales and thus are robust withrespect to noise. Persistent homology has had many successfulapplications, but there is room for improvement. For large datasets,computation of persistent homology often takes a significant amount oftime. Several approaches have been proposed to try to remedy this issue,such as witness complexes, but those approaches present their owndifficulties.

Speaker: Chuang Xu (Technical University Munich)

Title:

Analyzing walks with combinatorics and automata theory

Abstract:

The enumeration theorem by Chomsky and Schützenberger revealed

a significant intersection between the theory of automata and

enumerative combinatorics. Since then, much progress has been made in

both fields. However, their intersection remains unchanged in the sense

that no further enumeration theorem emerged beyond that of

Chomsky-Schützenberger. We survey the literature in both fields and

picture what it would look like to expand the intersection between them.

**Michael W. Stewart Monday 02 May 2022 3:30** pm Keller, room 302

Title: “A numerical method for solving the eigenvalue problem associated with neutron diffusion inside nuclear reactor cores”

Abstract: “In this talk we will explore a mathematical tool that might assist in tackling one problem in the design of nuclear reactors, namely a numerical method for finding solutions to the neutron diffusion equation during steady state operations. After a brief look at the physics of nuclear fission and the physical aspects of nuclear reactors we will derive a partial differential equation that can be used to model such systems in a time independent steady state. The use of finite element discretization allows us to find weak solutions to the eigenvalue problem which emerges, and so we review weak solutions, the discretization of continuous problems, and what is known about the linear systems that such discretization produces. The solutions of such a discretized problem will naturally differ from the exact solution, and so we set bounds on the errors that are introduced. A possible algorithm to solve the generalized eigenvalue problem, and some computational experiments will then be reviewed.”