Past SCAIM Seminars

Tue, 2018-02-13 12:30 - 13:30
Ray Walsh, Department of Mathematics, Simon Fraser University
Much is known about cloud formation and their behaviour at large scales (kilometers). Considerably less, in atmospheric science, addresses the fluid mechanics dictating smaller scale motions that determine the shapes of cloud edges.
Thu, 2018-01-25 11:00 - 12:00
Alexander Bihlo, Department of Mathematics and Statistics, Memorial University of Newfoundland
We derive a universal criterion for the preservation of the lake at rest solution in general mesh-based and meshless numerical schemes for the shallow-water equations with bottom topography. The main idea is a careful mimetic design for the spatial derivative operators in the momentum flux equation that is paired with a compatible averaging rule for the water column height arising in the bottom topography source term.
Tue, 2018-01-16 12:30 - 13:30
Timm Treskatis, Mathematics UBC

What if we could imitate spider silk glands to produce biodegradable materials with properties similar to rubber or plastic? In our interdisciplinary team of fluid dynamicists, chemical engineers and material scientists, my role as mathematician is to try and answer this question from the numerical perspective. In this context, I am working on a problem of multiphase flow that includes advection, diffusion, chemical reaction, osmosis and viscoplastic behavior.

Tue, 2017-11-21 12:30 - 13:30
Mike Irvine, Mathematics UBC
Complex individual-based models abound in epidemiology and ecology. Fitting these models to data is a challenging problem: methodologies can be inaccessible to all but specialists, there may be challenges in adequately describing uncertainty in model fitting, and the complex models may take a long time to run, requiring parameter selection procedures. Approximate Bayesian Computation has been proposed as a likelihood-free method in resolving these issues, however requires careful selection of summary statistics and annealing scheme.
Tue, 2017-11-14 12:30 - 13:30
Tony Wong, Mathematics UBC
The eikonal equation is a fundamental nonlinear PDE that find vast applications. One particular example is to compute geodesic distance on a curved surface through solving an eikonal equation defined on the surface (surface eikonal equations). However, there are only very few literatures on solving surface eikonal equations numerically, due to the complication from the surface geometry. In this talk, we present a simple and efficient numerical algorithm to solve surface eikonal equations on general implicit surfaces.
Tue, 2017-10-31 12:30 - 13:30
Tom Eaves, Mathematics UBC
Transitional phenomena are ubiquitous in fluid dynamics and other nonlinear systems; they occur whenever there are multiple states in which a system can reside. Frequently, we are able to investigate when and how a system transitions from one state to another by performing a linear stability analysis and obtaining critical thresholds for various parameters beyond which our original state becomes "unstable". However, there are numerous examples for which such an approach does not work.
Tue, 2017-10-17 13:30 - 14:30
Bamdad Hosseini, Department of Mathematics, Simon Fraser University
Statistical and probabilistic methods are promising approaches to solving inverse problems – the process of recovering unknown parameters from indirect measurements. Of these, the Bayesian methods provide a principled approach to incorporating our existing beliefs about the parameters (the prior model) and randomness in the data. These approaches are at the forefront of extensive current investigation. Overwhelmingly, Gaussian prior models are used in Bayesian inverse problems since they provide mathematically simple and computationally efficient formulations of important inverse problems.
Tue, 2017-10-03 12:30 - 13:30
Eldad Haber, Department of Mathematics and Earth and Ocean Science, UBC
In this talk we will explore deep neural networks from a dynamical systems point of view. We will show that the learning problem can be cast as a path planning problem with PDE constraint. This opens the door to conventional Computational techniques that can speed up the learning process and avoid some of the local minima.
Tue, 2017-09-19 12:30 - 13:30
Uri Ascher, Department of Computer Science, The University of British Columbia
Visual computing is a wide area that includes computer graphics and image processing, where the ``eyeball-norm'' rules. I will briefly discuss two case studies involving numerical methods and analysis applied to this area. The first involves motion simulation and calibration of soft objects such as cloth, plants and skin. The governing elastodynamics PDE system, discretized in space already at the variational level using co-rotated FEM, leads to a large, expensive to assemble, dynamical system in time, where the damped motion may mask highly oscillatory stiffness.
Tue, 2017-08-29 12:30 - 13:30
Michael Overton, Courant Institute of Mathematical Sciences, New York University
In many applications one wishes to minimize an objective function that is not convex and is not differentiable at its minimizers. We discuss two algorithms for minimization of nonsmooth, nonconvex functions. Gradient Sampling is a simple method that, although computationally intensive, has a nice convergence theory. The method is robust and the convergence theory has recently been extended to constrained problems. BFGS is a well known method, developed for smooth problems, but which is remarkably effective for nonsmooth problems too.
Templates provided by UBC Department of Physics & Astronomy

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