Bath Numerical Analysis seminar - upcoming

Fridays at 12.15 at Wolfson 4W 1.7. All talks will be broadcast on Zoom.

Link to Zoom meeting

Everyone is welcome at these talks.

Date Speaker Title
6 Feb 2026 Shi Jin (Shanghai Jiao Tong University)
Quantum Computations of Partial Differential Equations

Quantum computers are designed based on quantum mechanics principle, they are most suitable to solve the Schrodinger equation, and linear PDEs (and ODEs) evolved by unitary operators. It is important to explore whether other problems in scientific computing, such as ODEs, PDEs, and linear algebra that arise in both classical and quantum systems which are not unitary evolution, can be handled by quantum computers.

We will present a systematic way to develop quantum simulation algorithms for general differential equations. Our basic framework is dimension lifting, that transfers non-autonomous ODEs/PDEs systems to autonomous ones, nonlinear PDEs to linear ones, and linear ones to Schrodinger type PDEs-coined “Schrodingerization” — with unitary evolutions. Our formulation allows both qubit and qumode (continuous-variable) formulations, and their hybridizations, and provides the foundation for analog quantum computing which are easier to realize in the near term. We will also present dimension lifting techniques for quantum simulation of stochastic DEs and PDEs with fractional derivatives, and quantum machine learning. A quantum simulation software — “UnitaryLab” — will also be introduced.
13 Feb 2026 Mike Giles (University of Oxford)
MLMC analysis of the stochastic heat equation

As a first step in my application of Multilevel Monte Carlo (MLMC) methods to stochastic PDEs driven by space-time white noise, in this talk I investigate 3 different approaches to generating coupled noise samples for the stochastic heat equation, and use Fourier analysis to analyse the resulting MLMC correction variance for 3 different output quantities of interest. Two key conclusions are a) a finite element construction is better than a finite volume construction, and b) for some outputs, Richardson extrapolation is needed to obtain the optimal complexity with the finite element and spectral constructions. The analysis is supported by numerical results.
20 Feb 2026 Danny Barash (Ben-Gurion University)
3D shape alignment with partial overlap

Current systems achieve high local match quality but still break global structure under symmetry, large viewpoint and scale changes, repeated patterns, occlusion, and domain shift between sensors or modalities. Matches that look locally correct can violate the object layout and destabilize optimization. This shows up on standard semantic correspondence tests and in applied settings. A practical way forward is to introduce geometry where it helps most. We propose lightweight cues in the features that encode orientation and left versus right, and simple constraints in the matcher based on relative distances, angles, and neighborhood order. This keeps appearance strengths while restoring structural consistency across a wide range of conditions. We will evaluate on widely used benchmarks and release a small, reproducible codebase that others can adopt. We would like to aim towards: encoding orientation and left versus right in local descriptors, constraining the matcher with basic geometric relations, and packaging a clean testbed with ablations and clear evaluation for reuse.
27 Feb 2026 Lea Bogensperger (University of Zurich)
6 Mar 2026 Wenqi Zhu (University of Oxford)
13 Mar 2026 Georgios Exarchakis (University of Bath)
20 Mar 2026 Daniel Burrows (IMI and University of Bath)
Motion-enabled tomography via Gaussian Mixture Models

Recovering the physical properties of objects in motion is a fundamental challenge across various scientific and industrial applications. When motion is sufficient to provide a fully informative sinogram relative to a fixed source-receiver system, Computerised Tomography (CT) offers a powerful means for recovering object properties, enabling automation and access to otherwise hidden information. To address this, we propose a general time-varying parametric model for Gaussian Mixture Models (GMMs) that explicitly describes angular velocity, projectile motion, and morphology specific to each constituent Gaussian. Leveraging the closure of GMMs under the ray transform, we derive an exact forward model that allows for reconstruction by fitting directly to observed sinogram data. The utility of using GMMs as a basis for reconstruction comes from their ability to approximate complicated mathematical functions with a high degree of accuracy [Zickert, Öktem, Yarman, 2022] [Zickert, Yarman, 2021].

To implement this reconstruction, we utilize suitable loss functions and the automatic differentiation capabilities of PyTorch to efficiently compute parameter estimates that characterise the approximating GMMs. Alongside motion and tomography models applicable to Euclidean spaces of arbitrary dimension, we provide a fully operational gradient-based optimization algorithm tailored for two-dimensional scenarios. Our results demonstrate accurate and robust performance on challenging simulated datasets, providing a credible foundation for extending the algorithm to arbitrary dimensions.
20 Mar 2026 Kamran Arora (University of Bath)
27 Mar 2026 Alireza Naderi (University of Oxford)
17 Apr 2026 Amin Sabir (University of Bath)
17 Apr 2026 Matt Evans (University of Bath)
24 Apr 2026 Mostafa Meliani (University of Bath)
24 Apr 2026 María Ignacia Fierro-Piccardo (University of Bath)
1 May 2026 Massimiliano Tamborrino (University of Warwick)
Subscribe to seminar calendar

You can subscribe to the NA calendar directly from your calendar client, including Outlook, Apple’s iCalendar or Google calendar. The web address of the calendar is this ICS link which you will need to copy.

To subscribe to a calendar in Outlook:

  1. In Calendar view, select “Add Calendar” (large green +)
  2. Select “From Internet”
  3. Copy paste the ICS link, click OK, and click Yes to subscribe.

To subscribe to a calendar in iCalendar, please follow these instructions. Copy paste the ICS link in “web address”.

To subscribe to a calendar in Google Calendar:

  1. Go to link.
  2. On the left side go to "Other Calendars" and click on the dropdown.
  3. Choose "Add by URL".
  4. Copy paste the ICS link in the URL of the calendar.
  5. Click on "Add Calendar" and wait for Google to import your events. This creates a calendar with a somewhat unreadable name.
  6. To give a readable name to the calendar, click on the three vertical dots sign next to the newly created calendar and select Settings.
  7. Choose a name for the calendar, eg. Numerical Analysis @ Bath, and click back button on top left.

How to get to Bath See here for instructions how to get to Bath. Please email James Foster (jmf68@bath.ac.uk) if you intend to come by car and require a parking permit for Bath University Campus for the day.
Tips for giving talks

Tips for new students on giving talks

Since the audience of the NA seminar contains both PhD students and staff with quite wide interests and backgrounds, the following are some guidelines/hints to make sure people don't give you evil looks at lunch afterwards.

Before too much time passes in your talk, ideally the audience should know the answers to the following 4 questions:

  • What is the problem you're considering?
  • Why do you find this interesting?
  • What has been done before on this problem/what's the background?
  • What is your approach/what are you going to talk about?

There are lots of different ways to communicate this information. One way, if you're doing a slide show, could be for the first 4 slides to cover these 4 questions; although in this case you may want to revisit these points later on in the talk (e.g. to give more detail).

Remember:

  • "vertebrate style" (structure hidden inside - like the skeleton of a vertebrate) = good for detective stories, bad for maths talks.
  • "crustacean style" (structure visible from outside - like the skeleton of a crustacean) = bad for detective stories, good for maths talks.