CCP-NTH

AI for Nuclear Thermal Hydraulics Summer School

A five-day in-person summer school introducing machine learning as a practical engineering tool for nuclear thermal hydraulics, with a focus on structured data, surrogate modelling, uncertainty, and decision support.

About the summer school

This 5-day summer school introduces machine learning as a practical engineering tool for nuclear thermal hydraulics (NTH), with a focus on structured data, surrogate modelling, uncertainty, and decision support.

The programme is designed for PhD students, researchers, and practising engineers with experience in CFD, thermal hydraulics, and limited prior exposure to machine learning.

Places are limited, and registration will be confirmed following review.

This event is organised by CCP-NTH.

Programme

The programme is currently being finalised and will be updated regularly.

It will include:

• Introductory lectures on machine learning for nuclear thermal hydraulics
• Structured data analysis and engineering datasets
• Surrogate modelling approaches
• Uncertainty, model interpretation, and decision support
• Hands-on sessions and practical discussion

The latest programme information will be added here in due course.

Practical information

• Registration deadline: 5 June 2026
• Registration note: Registration may close earlier if oversubscribed.
• Accommodation: Accommodation can be provided if required.
• Enquiries: Wei Wang (wei.wang@stfc.ac.uk) or Yu Duan (yu.duan@sheffield.ac.uk)

Additional information

Further details on the venue, access, accommodation arrangements, registration, and other practical information will be updated in due course.

Code of Conduct: Participants are expected to behave professionally and respectfully throughout the summer school. Please read the event code of conduct here.