The JAMS is a weekly seminar with talks given by junior researchers on a topic in all areas in Mathematics. It is a great opportunity to share research, get feedback from other students and to become more confident in presenting work. We also provide drinks and snacks!
This year, there are two different types of talks:
Classic JAMS: Short talks, somewhat specialised and more focused on new results. A great opportunity to get know what fellow PhD students’ research is about.
Introductory JAMS: Longer talks, but more accessible and educational. These are meant to introduce you to the basic ideas of active research fields, and give you a broader picture of modern Applied Mathematics.
If you are interested in giving a talk (of either type) or have further questions, please email us at firstname.lastname@example.org. You can also sign up to our Mailing List to receive regular updates on these events!
Venue: Huxley 130
Time: Wednesday 4pm-5pm
(05/02/20)Rate induced tipping in dynamical systems
The concept of a tipping point (or critical transition) describes a phenomenon where the behaviour of a physical system changes drastically, and often irreversibly, compared to a relatively small change in its external environment. A number of generic mechanisms have been identified which can cause a system to tip. One such mechanism is rate-induced tipping, where the transition is caused by a parameter changing too quickly – rather than moving past some critical value. In this talk I will explain the mathematics behind rate-induced tipping and illustrate the concept with an ecological example.
(05/02/20)A Brief Introduction to Data-Driven Dimensionality-Reduction Methods
Instead of talking about my work directly, I plan to give a brief tour of some of the methods that myself and many other people use to extract low-dimensional models from high-dimensional data. Arguably the most well-known of these are POD and DMD, along with their numerous variants. I will also introduce SINDy, a method which is able to infer the underlying equation from a dataset using sparse regression methods. While many of these methods were initially inspired by fluid dynamics, they have already been adopted in other fields such as financial mathematics and the modelling of infectious diseases.
To view past seminars check here