Past Events

Strong cocomparability graphs are the reflexive graphs whose adjacency matrix can be rearranged by a simultaneous row and column permutation to avoid the submatrix with rows 01, 10. Strong cocomparability graphs form a subclass of cocomparability...

The first MMI in-person meeting will take place at UBC Vancouver, scheduled from September 26th to September 29th. The schedule is as follows: HQP Summit: September 26th evening to September 28th mid-day Working Groups Meetings: September 28th mid...

Fee Structure Room Rate: $189 CAD/night Tax and Service Charges: $26 CAD/night Nightly Rate: $215 CAD Total Cost for the Event Period (September 26th - September 29th): $645 CAD * If you would like to extend your stay, please contact programs@pims...

Lattice Yang-Mills theories are important models in particle physics. They are defined on the d-dimensional lattice ℤd using a group of matrices of dimension N, and Wilson loop expectations are the fundamental observables of these theories. Recently...

The evolution and maintenance of cooperation is a fundamental problem in evolutionary biology. Because cooperative behaviors impose a cost, Cooperators are vulnerable to exploitation by Defectors that do not pay the cost to cooperate but still...

Double dimers are superimposition of two perfect matchings. Such superimpositions can be decomposed into disjoint simple loops. The question we address is: as the graphs become large, in a 'typical' double dimer sample, do some of the loops diverge...

In this talk, we will discuss a well-known formula of Ramanujan and its relationship with the partial sums of the Möbius function. Under some conjectures, we analyze a finer structure of the involved terms. It is a joint work with Steven M. Gonek...

Fluid-structure interaction (FSI) is ubiquitous in nature and occurs at molecular to environmental scales, from the writhing of DNA in nucleoplasm, to the beating of cilia and flagella and the projection of lamellipodia and bleb-like protrusions by...

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 recent years, classical knowledge-driven approaches for inverse problems have been complemented by data-driven methods exploiting the power of machine and especially deep learning. Purely data-driven methods, however, come with the drawback of...