Compute Ontario Research Day 2014
The problem of calculation of electro and thermo static fields in an infinite homogeneous medium with a heterogeneous isolated inclusion (Kanaun et al) has shown to be reduced to the solution of integral equations for the fields inside the inclusion using Gaussian functions (V. Mazya) for the approximation of the unknown fields. Using this approach coefficients of the matrix of the discretized system will be obtained in closed analytical forms.
The effects of the microstructure of metal films on device performance and longevity have become increasingly important with the recent advances in nanotechnology. Depending on the application of the metal films and interconnects certain microscopic structures and properties are preferred over others. A common method to produce these films and interconnects is through electrodeposition. As with every process the ability to control the end product requires a detailed understanding of the system and the effect of operating conditions on the resulting product.
The goal of this research is to investigate theoretically the possibility of creating graphene-based semiconducting 2D heterosystems that allow tailoring of the band gap and creating states inside the gap by demand. Such systems are created in our computational experiment by depositing graphene on a layer of hexagonal boron nitride and adding hydrogen on top and bottom of the systems to passivate the dangling bonds and create covalent bonding between the layers of the system of interest.
Discontinuous Galerkin finite element (DGFE) methods combine advantages of both finite differences and finite elements approaches. These methods scale extremely well and they have been very successful in computational fluid dynamics. As such we would like to transpose them to the domain of relativistic astrophysics. Recently we have implemented DGFE methods in the Einstein Toolkit a large numerical relativity codebase used by hundreds of scientists around the world.
Electroencephalography (EEG) is a method for measuring brain activity by recording electrical fields at the scalp surface. Although it has the highest temporal resolution among brain imaging techniques it has low spatial resolution and is very sensitive to various forms of noise (e.g. movement artifacts electrical sources in the environment impedance artifacts and various biological artifacts typically generated from muscle activation).
Application of numerical simulations to quantum gravity are so far largely neglected yet they possess remarkable potential to learn more about the theory. For approaches that attempt to construct quantum spacetime from fundamental microscopical building blocks e.g. spin foam models the collective behaviour involving many building blocks is unexplored.Therefore we numerically simulate the collective dynamics of many of these building blocks using coarse graining techniques i.e.
Many biological data sets and relationships can be modeled as graphs. Understanding how structure of these graphs relates to biological function is essential for understanding underlining mechanisms of disease and for aiding drug discoveries. Vertices of biological graphs represent individual entities such as genes and proteins. Edges represent the relationship between two cellular components such as physical and functional interactions. A challenging problem in the post-genomic era is graph comparisons as they are large typed complex and evolving.
Buoyancy driven flows at the top of the ocean or bottom of the atmosphere are inherently different from their interior dynamics. Oneidealized model that has recently become very popular to idealizethese surface flows with strong rotation is Surface Quasi-Geostrophic (SQG) dynamics. This model is appropriate for large-scale dynamics and assumes the motion is in near geostrophic and hydrostatic balance.
Along with the development of computational resources computational fluid dynamics (CFD) has evolved in resolving the finest length scales and smallest time scales of the flow. Direct numerical simulation (DNS) resolves the finest flow scales known as Kolmogorov length scales which are responsible for the dissipation of the energy transferred from the large and intermediate length scales. However DNS simulations are computationally costly and demand very powerful resources which are not widely available to this day.