Events
Department of Mathematics and Statistics
Texas Tech University
The importance of the Hall term in plasma physics has been well known since the early work of James Lighthill in the 1960s. On the other hand, since the early 2000s, there has been a stream of analytical, computational, and experimental evidence establishing that Resistive and Hall terms incorporate the “minimal amount of physics” required to reproduce magnetic reconnection rates observed in practice.
In this talk, I present a structure-preserving method for the compressible Resistive Hall magnetohydrodynamics model. The differential operator is split into two parts: a hydrodynamic part consisting of the compressible Euler equations and a magnetic part consisting of a system that couples the Lorentz force to the induction equation. The method uses continuous Lagrange elements for the Euler part and a curl-conforming finite element space for the magnetic part. The hydrodynamic part preserves the positivity of the density and internal energy, the conservation of total energy, and the minimum principle for the specific entropy. Owing to the choice of finite elements, the magnetic part preserves the divergence involution constraint. The magnetic part of the system is solved by the Crank-Nicolson method, which requires using Newton's method. Coercivity estimates for the Jacobian of the corresponding Newton iteration are presented. We introduce a high-order artificial resistivity to improve the conditioning of the nonlinear residual and the invertibility of the corresponding Jacobian. Several challenging benchmarks, including a smooth whistler wave, the Orszag–Tang vortex for comparing resistive MHD with resistive Hall-MHD, and a magnetic reconnection problem, are solved to validate the method's robustness and accuracy.
This presentation is archived in the Mediasite catalog at this url ...
mediacast.ttu.edu/Mediasite/Play/da582ce4ae0a4c9988d3f7e1ff937ecf1d
Nature optimizes shape, and geometry reveals the rigid structures that emerge at these extremes. To understand these structures, my research program operates at the interface of geometry, topology, and analysis. I will begin this talk by discussing Hamilton’s Ricci flow and our recent breakthroughs in identifying and classifying singularity models. Guided by symmetry principles, we uncover a surprising connection between symplectic mechanics and complex geometry to address fundamental questions in the field.
Our work above contributes to a global program aimed at generalizing Perelman’s Ricci-flow-with-surgery framework, which resolved the Poincaré Conjecture, to higher dimensions. As an application, I will explain how the Ricci flow deforms a closed Riemannian manifold into a round sphere under a natural curvature assumption in any dimension. That establishes our complete resolution of a landmark conjecture proposed by S. Nishikawa. Next, I will highlight our work on the variational stability of critical points via the Morse index, where we introduce an innovative spectral technique to decouple and analyze multiple physical constraints. Finally, I will outline my recent efforts translating pure geometric theories to applied frontiers, highlighting a project on mesh transformations for tensor networks supported by the U.S. Department of Energy and Los Alamos National Laboratory.
This presentation is archived in the Mediasite catalog at this url ...
mediacast.ttu.edu/Mediasite/Play/235ab27e6ed04f808bb054f9304b3e2e1d
Secret Sharing is a cryptographic technique which deals with sharing a secret to some set of users in such a way that sets of users that are authorized can recover the secret while sets of users that are not authorized have no information about the secret.
We will discuss a protocol based on matrix trace and prove its correctness and security. Finally, we will discuss the share size.
 | Thursday Sep. 10 6:30 PM Math 010
| | Mathematics Education Math Circle Erhan Guler Mathematics and Statistics, Texas Tech University
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Outreach program for advanced middle and high school math studentsModern generative models can be viewed as dynamic transports that transform a simple source distribution into a complex target distribution. In the stochastic setting, the Schrödinger bridge provides a classical and computationally tractable formulation: among all diffusions matching two prescribed marginal laws, it selects the one closest to Brownian motion. However, the classical Schrödinger bridge learns a drift while keeping the volatility fixed, which is restrictive in applications where uncertainty itself is part of the signal, notably in finance.
In this talk, I will introduce the Schrödinger–Bass Bridge, a semimartingale optimal transport problem that interpolates between the Schrödinger bridge and the Bass martingale transport. The resulting optimal dynamics learn both drift and volatility. I will describe the associated Schrödinger–Bass system, its interpretation as a Bass transport of a Schrödinger bridge, and the resulting fixed-point algorithm. I will then discuss applications to generative modeling, first for image synthesis and then for financial time series. In the time-series setting, the method leads to SBBTS, a sequential generative model designed to preserve temporal dependence and stochastic volatility. Numerical experiments illustrate its ability to recover hidden Heston-type volatility and correlation structures and to improve downstream forecasting through synthetic data augmentation.
This presentation has been archived in the TTU Mediasite catalog ...
mediacast.ttu.edu/Mediasite/Play/40e41fa08bed4e82b7561dbe488046981d