Gabriele Scrivanti
MaLGa, University of Genoa
Title TBC
Abstract
TBC
- When
- Wednesdays, 14:00
- Where
- Charles Carter A15
- Audience
- Postgraduates and staff
Michaelmas term · 5 October–11 December 2026
Upcoming seminars
More seminars will be added
Titles and abstracts will appear here as they are confirmed.
Date & time
Speaker
TitleSelect a title to show its abstract
Jixiang Qing
Lancaster University
Title TBC
Abstract
TBC
Hong Duong
University of Birmingham
Model Reduction of Complex Systems
Abstract
Complex systems in nature and in applications (such as molecular systems, crowd dynamics, swarming, opinion formation, just to name a few) are often described by systems of stochastic differential equations (SDEs) and partial differential equations (PDEs). It is often analytically impossible or computationally prohibitively expensive to deal with the full models due to their high dimensionality (degrees of freedom, number of involved parameters, etc.). It is thus of great importance to approximate such large and complex systems by simpler and lower dimensional ones, while still preserving the essential information from the original model. This procedure is referred to as model reduction or coarse-graining in the literature. In this talk, I will present methods for qualitative and quantitative coarse-graining of several SDEs and PDEs, in the presence or absence of a scale-separation.
Teresa Klatzer
Lancaster University
Title TBC
Abstract
TBC
Cameron Smith
University of Bath
Title TBC
Abstract
TBC
Gabriel Stoltz
Ecole des ponts & Inria Paris
A mathematical analysis of autoencoders for (un)supervised dimensionality reduction
Abstract
Sampling high dimensional probability measures is often made difficult by the multimodality of the target probability distribution. Markov chain Monte Carlo methods need to pass through low probability regions to switch from one mode to another, which is a rare event. An approach to making these transitions less rare is to identify a few selected (nonlinear) degrees of freedom of the system, which are at the origin of the slow mixing behavior, compute the associated free energies, and perform some importance sampling based on the latter function. Various tools have now recently been developed in molecular simulation to automatically find the most relevant nonlinear degrees of freedom hindering sampling, based on machine learning tools such as autoencoders. I will present a methodology to leverage these models for better sampling, and will also provide a mathematical analysis of the approach, relating it to principal manifolds and providing an interpretation based on conditional expectations. I will also discuss recent work in combining these techniques with supervised dimensionality reduction approaches such as Fisher's LDA. The results will be illustrated on biologically relevant systems such as HSP90.
About the series
Research across applied mathematics and AI
The MARS seminar series covers applied mathematics and the mathematics underpinning AI. Talks range across mathematical modelling, numerical methods, optimisation, uncertainty quantification and machine learning, usually with a real-world problem in view.
We invite both internal and external speakers. Talks are intended for a broad audience of researchers and postgraduate students from across Lancaster.
Attend
Practical information
Format
Talks normally last 40–45 minutes, followed by 5–10 minutes for questions.
Venue
Charles Carter A15, Lancaster University.
Recordings
Selected talks are available on the MARS YouTube channel.
Contact
For enquiries, contact Maciej Buze.