Simon Fraser University

A well-known conjecture in physical literature states that high frequency waves propagating over long distances through turbulence eventually become complex Gaussian distributed. The intensity of such wave fields then follows an exponential law...

The practical performance of an optimization method depends on details such as using good step sizes. Strategies for setting step sizes are generally limited to hyperparameter tuning (for a fixed step size), step size schedules and line searches. For...

An $H$-intersecting family of 3-uniform hypergraphs on $n$ labelled vertices is a family of hypergraphs $\mathcal{F}$ such that for any pair of hypergraphs $G_1, G_2 \in \mathcal{F}$, the intersection $G_1 \cap G_2$ contains a copy of $H$ as a...

Operator learning is an emerging field at the intersection of machine learning, physics, and mathematics, that aims to discover properties of unknown physical systems from experimental data. Popular techniques exploit the approximation power of deep...

The Vlasov-Poisson system is a non-linear PDE describing the mean-field time-evolution of particles forming a plasma or a galaxy. In this talk I will present uniqueness criteria for the Vlasov-Poisson equation in the classical and semi-relativistic...

We construct a convex body $K$ in $R^n$, $n \ge 5$, with the property that there is exactly one hyperplane $H$ passing through $c(K)$, the centroid of $K$, such that the centroid of $K \cap H$ coincides with $c(K)$. This provides answers to questions...

In recent years, there has been growing promise in coupling machine learning methods with domain-specific physical insights to solve scientific problems based on partial differential equations (PDEs). However, there are two critical bottlenecks that...

Stellarators are promising magnetic fusion devices for electricity generation, because the dynamics of the hot fusion fuel - called a plasma - is largely determined by external control, as opposed to dynamical self-organization, as is the case for...

The cosine measure is a tool that tells you how well a set of directions is covering the space $\mathbb{R}^n$. In this talk, we extend the concept of cosine measure by defining the cosine measure relative to a subspace. This novel definition might be...

Firmly nonexpansive operators are important in Convex Analysis and Optimization and Algorithms. It is a special case of averaged operators. We classify averaged operators, firmly nonexpansive operators, and proximal mappings by the BBW modulus of...