Data Assimilation for Discontinuous State Variables

Data assimilation is a method for combining available observations with a background from numerical model, to find the best estimate of the system, which is crucial for improving environmental variable prediction. However, commonly used Gaussian distribution assumption could introduce biases for state variables with discontinuous profiles, such as sea ice thickness with sharp features. In this talk, we focus on the design of non-Gaussian prior based on various statistics of the state variables.

Modeling neuronal synchrony

The Graphs and Networks seminar will meet Mondays at 9:30 am via Microsoft Teams.
Greg Constantine will start by giving 3 talks on classification attempts of graphs of maximal complexity. The triangle-free strongly regular graphs -- all subgraphs of the Higman-Sims graph -- are proved to be instances of such graphs of maximal complexity. A series of conjectures and some stubborn research issues will be brought to attention.

Jon Rubin will then give several talks on networks. Sevak Mkrtchyan will also present a series of talks.