Data-Driven Numerical Modelling of Material Behaviour for Finite Element Simulation

About the Project

Modern infrastructure increasingly relies on materials whose composition varies due to local resources, alternative binders, and sustainability-driven formulations. While essential for reducing environmental impact, this variability introduces significant uncertainty in material behaviour, challenging traditional modelling approaches used in large-scale engineering applications.

This PhD project aims to develop a general, data-driven framework for the automatic discovery of interpretable material behaviour laws for use in finite element software. The research will integrate full-field experimental measurements—such as Digital Image Correlation (DIC)—with synthetically generated data and advanced machine-learning techniques to identify physically meaningful constitutive relationships directly from data, ensuring both predictive accuracy and interpretability.

The project places strong emphasis on rigorous mechanics, inverse modelling, and computational implementation, producing models that are transferable across materials and operating conditions. The resulting methodology will support reliable prediction of material response in safety-critical and large-scale infrastructure subjected to mechanical and environmental loading.

This PhD provides advanced training in computational mechanics, data-driven modelling, and material characterisation, equipping graduates with skills highly valued in international research institutions and advanced engineering industries, particularly within the energy, nuclear, and infrastructure sectors worldwide.

Eligibility Requirements

We welcome applications from candidates worldwide. Applicants should have:

• 1st or 2:1 degree in Engineering, Materials Science, Physics, Chemistry, Applied Mathematics, or other Relevant Discipline.
• Experience in numerical modelling/materials.
• Strong mathematics, physics, and computer programming skills.

If English is not your first language, you may be required to provide evidence of English language proficiency (e.g. IELTS or TOEFL), in accordance with the University of Sheffield requirements.

Research group, collaborations and contact:

The project is embedded within a vibrant and internationally recognised research group, with established collaborations with major industrial partners—EDF Energy, Unipart Construction Technology, and Sellafield Ltd—as well as European academic partners. These collaborations ground the research in real engineering challenges across the energy, nuclear, and infrastructure sectors, while retaining a strong fundamental research focus.

For more details please contact Dr Giacomo Torelli within the School of Mechanical, Aerospace and Civil Engineering at g.torelli@sheffield.ac.uk

Funding Notes

This project is offered on a self-funded or externally funded basis. Applicants must demonstrate access to suitable financial support from personal, national, or institutional sources to cover tuition fees and living expenses. Unfortunately, university funding is not available for this project at this time.