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.
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 be open from mid March and 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
