University of Alberta

TBA

The interactions among species in a food web or food chain are inherently complex. Mathematical models provide a framework to closely represent the dynamics observed in ecosystems. However, capturing this complexity within a single mathematical setup...

To effectively reduce methane emissions from oil sands tailings ponds, it is crucial to comprehensively understand the characteristics of methane input and the specific contribution of each element to the overall emission. In this procedure, one must...

Informally speaking, sampling discretization studies how well one can replace the computa- tion of integral L p norms for a given class of functions by the evaluation of these functions at a fixed small set of points. On the one hand, such problems...

Understanding mechanisms of coexistence is a central topic in ecology. Mathematical analysis of models of competition between two identical species moving at different rates of symmetric diffusion in heterogeneous environments show that the slower...

We discuss issues arising from methodological developments related to inverse probability weighting and model-based prediction with non-probability survey samples, with focuses on the validity of estimation procedures and the impact of key...

A good way to understand the structure of a finite group is through its representations. The family of representations of a group on a fixed field has several properties: it is closed by finite direct sums, by tensor products, it contains the dual...

Physics-Informed Neural Networks (PINNs) have received much attention recently due to their potential for high-performance computations for complex physical systems, including data-based computing, systems with unknown parameters, and others. However...

In what will hopefully become a department tradition, the first colloquium of the year this Friday will consist of introductions of ten new post-doctoral fellows who have joined the department in the past few months. Each PDF will give a 5-minute...

Recently, Machine Learning (ML) approaches to data assimilation and modelling have been very successful in interpreting large amounts of data, such as human behavior prediction, marketing, etc. However, direct applications of machine learning methods...