Welcome
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.
Topics will include, but not limited to
- ML for inverse problems
- ML for differential equations
- Operator learning
- Neural ODEs and reconstruction of dynamics
- Generative modelling
- Geometric deep learning
- Applications to:
- Geo and environmental sciences (including weather and climate forecasting)
- Medical imaging
- Control theory
- electromagnetics
- drug discovery
Please contact the organizers at yaguchi (at) pearl.kobe-u.ac.jp or scml26-committee (at) geom.jp with any questions.
Conference venue
Claverton Down, Bath BA2 7AY, UK
Accommodation
Delegates will need to book their own accommodation in Bath, which has a wide range of options to suit all budgets. For further information on accommodation options, please visit International conference on scientific computing and machine learning, 14-17 September 2026, University of Bath | M4DL
Call for contributed talks
We welcome submissions of contributed talks from all related areas with the above topics.
- Please submit your extended abstract outlining your presentation. Submitted abstracts will be reviewed by the Program Committee. All accepted abstracts will be made available to conference participants.
- Submission of papers that are under review or have been recently published in a conference or a journal is allowed.
- At the time of submission, authors can choose between an oral or poster presentation. However, if there are many applicants for oral presentations, the presentation may be changed to a poster presentation based on the results of the peer review.
- Submissions should not exceed one page, excluding references and supplementary materials.
- All submissions must be in the pdf format based on the SCML style file. Please see template.pdf in the SCML style file for other details.
How to submit a paper
The submission site will be available soon.
Important Dates
- Contributed talks submissions due: 8 May, 2026
- Notification to authors: mid-June, 2026
Registration
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. 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.
Registration will be via the University of Bath. Please visit this page to find out more about how to register: International conference on scientific computing and machine learning, 14-17 September 2026, University of Bath | M4DL
Registration fee
£50 GBP
Organizers
- Christopher J. Budd OBE (University of Bath)
- Matthias J. Ehrhardt (University of Bath)
- Mizuka Komatsu (Kobe University)
- Yury Korolev (University of Bath)
- Amy Lunt (University of Bath)
- Chaoyu Liu (University of Cambridge)
- Davide Murari (University of Cambridge)
- Daisuke Tagami(Kyushu University)
- Baige Xu (Kobe University)
- Takaharu Yaguchi (Kobe University)
This conference is supported by EPSRC Programme Grant Maths4DL on “the Mathematics of Deep Learning,” 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.”
The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.
