In recent years, machine learning methods for scientific computing have attracted much attention. Many methods are a combination of machine learning and/or theories of physics and/or computational mathematics.
This conference aims to showcase the latest research in these areas, which have been fragmented while pursuing research in the same direction, to bridge the gap between them, and to promote collaboration.
The conference will comprise keynote and invited talks, tutorials, contributor talks and poster sessions. There will be a drinks reception as well as a conference dinner. More information about the schedule will be posted here in due course.
| Keynotes | |
| Speaker | Topic |
| Gabriele Steidl | tbc |
| Kirstine Dale | Forecasting the future: embedding AI in the UK’s weather service |
| Hyung Ju Hwang | Learning dynamical systems: neural solvers for kinetic equations and data-driven inference |
| Elena Celledoni | tbc |
| Olga Mula | tbc |
| Tom Pock | tbc |
| Juan-Pablo Ortega | Kernel methods for structure-preserving learning |
| Qianxiao Li | Constructing macroscopic dynamics using deep learning |
| Dimitris Giannakis | tbc |
| Invited speakers | |
| Speaker | Topic |
| Caroline Moosmuller | tbc |
| Sofya Maslovskaya | Application of structure preserving numerical methods in deep learning |
| Priscilla Canizares | tbc |
| Katarzyna Michalowska | Neural operator learning for physical systems |
| Leon Bungert | New developments in graph-based learning |
| Jeremias Sulam | Sampling beyond scores: proximal diffusion models |
| Matt Colbrook | Data-driven resonances and tipping points for dynamical systems |
| Yeonjong Shin | tbc |
| Noboru Isobe | A control theoretical view of mean-field transformers |
| Domènec Ruiz-Balet | tbc |
| Matt Thorpe | Gradient flows on graphs with applications in semi-supervised learning |
| Jemima Tabeart | Data assimilation and machine learning: challenges and opportunities |
| Alice Cicirello | tbc |
| Tutorials | |
| Speaker | Topic |
| Yuka Hashimoto | C*-algebra for machine learning – applying C*-algebras to kernel methods and neural networks |
| Tutorial 2 tbc | tbc |
Alongside our programme of keynote and invited speakers, we will have sessions for short contributed talks and posters. You are invited to submit an abstract for consideration. Please visit SCML2026 – International Conference on Scientific Computing and Machine Learning to find out more about how to submit your abstract. Submissions will close on 8 May.
All delegates, including those giving a contributor talk or presenting a poster, will need to register and pay a small registration fee.
There is also the option of joining the conference dinner which will take place on Wednesday 16 September at the University of Bath.
To register, please visit the University of Bath online shop.
We encourage early registration, however we appreciate that those submitting abstracts may not wish to register until a response has been received, which should be in early June.
The conference will be held at the University of Bath on the Claverton Down campus. To find out more about the location of the campus and travel options, please visit our Travel advice page.
There is a wide variety of accommodation available in Bath but it does get very busy, therefore we advise early booking. We will share further guidance on hotel options on this page in due course.
This conference is supported by EPSRC Programme Grant on the Mathematics of Deep Learning (Maths4DL), JST CREST Prediction Mathematical Foundation, Operator Learning Based on Geometric Classical Field Theory and Infinite Dimensional Data Science, and by JST ASPIRE Deep scientific computing: integration of physical structure and deep learning through mathematical science.
Please visit SCML2026 – International Conference on Scientific Computing and Machine Learning for further details
