Calendar

Posted September 3, 2024

Control and Optimization Seminar Questions or comments?

Denis Efimov, University of Lille
Homogeneity with Respect to a Part of Variables and Accelerated Stabilization

The presentation addresses the problem of transforming a locally asymptotically stabilizing time-varying control law to a global one with accelerated finite/fixed-time convergence rates. The approach relies on an extension of the theory of homogeneous systems to homogeneity only with respect to a part of the state variables and on the associated partial stability properties. The proposed control design builds upon the kind of approaches first studied in [MCloskey and Murray,1997] and uses the implicit Lyapunov function framework. A sampled-time implementation scheme of the control law is also presented and its properties are characterized. The method is illustrated by finite-time and nearly fixed-time stabilization of a nonholonomic integrator.

Posted September 13, 2024

Combinatorics Seminar Questions or comments?

Bryce Frederickson, Emory
Turán and Ramsey problems in vector spaces over finite fields

Abstract: Turán-type problems ask for the densest-possible structure which avoids a fixed substructure H. Ramsey-type problems ask for the largest possible "complete" structure which can be decomposed into a fixed number of H-free parts. We discuss some of these problems in the context of vector spaces over finite fields. In the Turán setting, Furstenberg and Katznelson showed that any constant-density subset of the affine space $AG(n,q)$ must contain a $k$-dimensional affine subspace if n is large enough. On the Ramsey side of things, a classical result of Graham, Leeb, and Rothschild implies that any red-blue coloring of the projective space $PG(n-1,q)$ must contain a monochromatic k-dimensional projective subspace, for n large. We highlight the connection between these results and show how to obtain new bounds in the latter (projective Ramsey) problem from bounds in the former (affine Turán) problem. This is joint work with Liana Yepremyan.

Posted September 19, 2024

Actuarial Student Association

Actuarial Student Association Meeting

We will have a guest visitor Aimee Milam from CareSource. Pizza will be served.

Posted August 30, 2024
Last modified September 16, 2024

Informal Geometry and Topology Seminar Questions or comments?

Rachel Meyers, Louisiana State University
TBD

Posted September 15, 2024

Conference

SIIT-LSU Conference on Analysis and PDE In Honor of Igor E. Verbitsky’s Retirement

This is an Analysis and PDE international joint conference between LSU and Sirindhorn International Institute of Technology (SIIT), Thailand, in honor of Professor Igor E. Verbitsky’s Retirement. The conference will take place virtually via Zoom and everyone is invited to participate. All the conference materials are now available on the conference website: https://sites.google.com/view/siit-lsu/

Posted August 27, 2024

Control and Optimization Seminar Questions or comments?

Jean Auriol, CNRS Researcher, L2S, CentraleSupélec
Robust Stabilization of Networks of Hyperbolic Systems with Chain Structure

In this talk, we focus on recent developments for the stabilization of networks of elementary hyperbolic systems with a chain structure. Such a structure arises in multiple industrial processes such as electric power transmission systems, traffic networks, or torsional vibrations in drilling devices. The objective is to design feedback control laws that stabilize the chain using the available actuators and sensors. The different systems composing the network are called elementary in the sense that when taken alone, we know how to design stabilizing output-feedback control laws. We will first consider the case where the actuators and sensors are available at one end of the chain. Using appropriate state predictors, we will present a recursive approach to stabilize the whole chain. Then, we will focus on the case where the actuators and sensors are only available at the junction between two subsystems composing the chain. We will show that such a configuration does not always guarantee the controllability of the chain. Under appropriate controllability/observability conditions, we will design simple stabilizing control laws. Our approach will be based on rewriting the system as Integral Delay Equations (IDEs) with pointwise and distributed control terms. Finally, we will show how the proposed techniques can be used to develop output feedback control laws for traffic flow on two cascaded freeway segments connected by a junction.

Posted September 19, 2024

LSU SIAM Student Chapter

Meet & Greet with Jon Loftin (MathWorks)

Pizza will be served. Jon Loftin will present on "Deep Learning with MATLAB: A Visual Approach" after the lunch.

Posted September 17, 2024

Combinatorics Seminar Questions or comments?

Matthew Kroeker, Waterloo
Unavoidable flats in matroids representable over a finite field

For a positive integer k and finite field F, we prove that every simple F-representable matroid with sufficiently high rank has a rank-k flat which either is independent, or is a projective or affine geometry over a subfield of F. As a corollary, we obtain the following Ramsey theorem: given an F-representable matroid of sufficiently high rank and any 2-colouring of its points, there is a monochromatic rank-k flat. This is joint work with Jim Geelen and Peter Nelson.

Posted September 19, 2024

LSU SIAM Student Chapter

Deep Learning with MATLAB: A Visual Approach

Deep learning is quickly becoming embedded in everyday applications. It’s becoming essential for students and educators to adopt this technology to solve complex real-world problems. MATLAB and Simulink provide a flexible and powerful platform to develop and automate data analysis, deep learning, AI, and simulation workflows in a wide range of domains and industries. In this workshop we will introduce deep learning with MATLAB. We will utilize a previously trained network and modify it, using the MATLAB Deep Network Designer. The Deep Network Designer allows you to interactively build, visualize, and train neural networks. Individuals can generate the code for the neural network and finetune parameters. Users can use popular pre-trained networks or construct their own. We will also look at the MATLAB Classification Learner to run several models on a single data set. These visual approaches create a more efficient workflow.