Welcome the Data and Dynamics Working Group at the University of Arizona, part of our NSF RTG in Data-Driven Discovery.
For the Spring 2024 semester, the seminar is co-organized by Kevin Lin, Alexa Aucoin, Robert Ferrando, and Ben Stilin. For previous semesters, see the following:
Meetings will be held on Mondays from 2:30 -- 3:45 pm in room MATH 402 and over Zoom. To be added to our email list, please contact one of the organizers.
Speakers: Christopher Koh
Title: Actor Physicists: Physics Informed Reinforcement Learning for Swimming in Turbulent Environments
Abstract: Turbulent diffusion separates particles placed initially close to each other. How much swimming efforts are needed to keep a particle sufficiently close to its passively advected partner? How to optimally balance the efforts with the goal? We answer the questions analyzing three different strategies, physics- uniformed, physics informed, and analytic strategies in the case of an active particle placed in a large scale turbulent flow and swimming towards its passive partner.
Speakers: Addie Harrison and Rebekah Saucier
Title: Introduction to ANSYS Fluent
Abstract: In this presentation, we will give an introduction to ANSYS, a computational fluid dynamics software. This tutorial will specifically go through the use of ANSYS fluent for computation of external flow around a rigid 3D structure. We will give a brief overview of ways to access and use ANSYS, setting up the geometry, meshing the structure in ANSYS, setting up the computation, creating report definitions (drag coefficient, drag force, etc.), and visualizing results.
Speaker: Andrew Arnold
Title: Missing the messy mesh mishaps
Abstract: In the first part of a series on the practical numerical solution of PDEs, I will present some insight on the questions of: a. What is a mesh? and b. How do we assess if a particular mesh is good for solving a problem?, largely based on the first chapter of "Delauney Mesh Generation" by Siu-Wing Cheng, et al. After this dose of theory, we'll get some hands-on experience with a tutorial on making and assessing a mesh using the open-source meshing software Gmsh. You can download the latest version at https://gmsh.info/ in preparation!
Speaker: Alexa Aucoin
Topic: Granger Causality
Abstract: I will give a talk on Granger Causality, a statistical test for determining "causal" relationships for time-series data. We will start with an introduction to the early definitions and applications of Granger, focusing on its assumptions and caveats. Afterwards, I will discuss recent improvements and extensions of Granger Causality using machine learning.
Speaker: Ziao Chen, University of Missouri
Title: Dimensionality Reduction in Neuroscience
Abstract: There has been rapid development and increasing use of technologies for recording from large numbers of neurons. It is important to study a population of recorded neurons beyond studying each neuron individually. Many recent studies have used dimensionality reduction to analyze the population activities that are not apparent at the level of individual neurons. I will discuss some dimensionality reduction methods commonly applied to population activity, starting with basic covariance methods such as principal component analysis (PCA) and factor analysis (FA). Then I will focus on a time series method called Gaussian process factor analysis (GPFA) which unifies the smoothing and dimensionality reduction operations in a common probabilistic framework and allows to apply on single-trial population activity. If time permits, I will briefly introduce other time series methods, such as latent linear/nonlinear dynamical systems.