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Monday, April 29, 2024 10:00 am - 11:00 am EDT (GMT -04:00)

PhD Comprehensive Exam | Tianlang Luo, Stochastic Dynamics of and ENSO Model

El Niño–Southern Oscillation (ENSO) is a climate phenomenon occurring every 3-7 years in the tropical Pacific. It is a pattern shifting between anomalous warm and cold events within equatorial Pacific, with irregularity in amplitude, duration, temporal evolution, and spatial structure. This research focuses on a specific ENSO model developed by Majda and coworkers, where state-dependent stochastic wind bursts and nonlinear advection of sea surface temperature are coupled to a simple ocean–atmosphere model that is otherwise deterministic, linear, and stable.

The human heart is a complex system that can undergo a critical transition to an abnormal rhythm, known as a cardiac arrhythmia. How to predict or assess the risk of cardiac arrhythmia in individual patients with heart disease is not clear. In this talk, Dr. Thomas Bury will demonstrate how deep learning can be combined with mathematical models of the heart to (i) improve prediction of an arrhythmia known as alternans, and (ii) discover mechanisms that can lead to arrhythmia.

Complex systems consist of a collection of devices with local dynamics and control that are coupled by physical or cyber networks. Examples include the electrical power grid, communication systems, and networks of mobile robots and autonomous vehicles. Such systems provide essential services to society but are notoriously challenging to analyze or control due to their large scale and complexity. The talk will discuss new methods to enable safe, reliable, and desired operation of complex systems.

The theory of non-regular separation is examined in its geometric form and applied to the bi-Helmholtz equation in the flat coordinate systems in 2-dimensions. It is shown that the bi-Helmholtz equation does not admit regular separation in any dimensions on any Riemannian manifold.

Parallel-in-time methods such as multigrid-reduction-in-time (MGRIT) and parareal utilize an iterative multilevel multigrid structure to solve linear systems of differential equations by solving a coarse-grid problem, which is less costly while also approximating the original, fine-grid problem. Such methods have been successfully applied to speeding up the solution of parabolic PDEs.