Cracks and Code: From High-Fidelity Simulations to Fast Scientific Machine Learning Models
This is a fully-funded 4-year PhD position based in the HetSys Centre for Doctoral Training at the University of Warwick.
Project outline
When metals experience extreme events, such as shock waves, high-speed impacts, or rapid deformation, they can fail suddenly in ways that remain difficult to predict. In this project, you will investigate how cracks initiate and grow in metals under these high-rate conditions. You will use large-scale simulations (both atomistic and continuum) to study how damage forms and localises, and then develop scientific machine-learning models that can reproduce these processes far more efficiently.
Your results will help create faster more reliable tools for predicting material failure in real engineering applications.
Please note that due to the nature of AWE-NST's work, nationality restrictions apply to applications for this project
Supervisors
Primary: Dr Emmanouil Kakouris (Engineering)
Dr Peter Brommer (Engineering)
Project Partner: AWE-NST
This PhD will tackle the challenge of predicting ductile fracture under extreme loading by:
- Developing a high-rate fracture numerical model that incorporates plasticity, strain localisation, rate-dependence, and temperature effects [1]
- Linking continuum model parameters to atomistic simulations to ensure the model remains physically meaningful across scales [2]
- Using scientific machine learning to accelerate simulations and enable rapid exploration of material behaviour under shocks [3].
- Validating your models against experimental data (e.g., spall tests) and applying them to engineering-relevant loading scenarios [4].
By the end of the PhD, you will have:
- A validated high-rate fracture model implemented inside an existing open-source code and capable of simulating strain localisation, void growth, and dynamic crack propagation
- A scientific machine learning surrogate that reproduces high-fidelity simulations at a fraction of the cost
- New physical insights into how microstructural mechanisms control failure at high strain rates
- Open-source or industrially deployable software, plus high-impact publications in computational mechanics or materials modelling.
Through this PhD, you will gain advanced expertise and transferable skills for careers in academia, research, or industry, while joining the collaborative HetSys community at the forefront of computational science and engineering. You will:
- Master advanced techniques in computational modelling, scientific machine learning, and data-driven materials science
- Gain practical experience using high-performance computing and developing scientific software to solve real-world challenges.
- Build strong communication, leadership, and collaboration skills through interdisciplinary training with HetSys, preparing you to become a confident innovator and future leader in your field.
These skills position you for careers in AI research, computational materials science, national laboratories, tech industry or academic research. The HetSys training provides a foundation for these skills through dedicated courses and cohort activities.
[1] Zhang et al., J. Mech. Phys. Solids 172 (2023) 105186, https://doi.org/10.1016/j.jmps.2022.105186
[2] Budarapu P. R., Rabczuk T., J. Indian Inst. Sci. 97 (2017) 339–376, https://doi.org/10.1007/s41745-017-0036-2
[3] Saha et al., Comput. Methods Appl. Mech. Eng. 448 (2026) 118493, https://doi.org/10.1016/j.cma.2025.118493
[4] Yin et al., Int. J. Impact Eng. 183 (2024) 104803, https://doi.org/10.1016/j.ijimpeng.2023.104803
We require at least a II(i) honours degree at BSc or an integrated masters degree (e.g. MPhys, MChem, MSci, MEng etc.) in a physical sciences, mathematics or engineering discipline. We do not accept applications from existing PhD holders.
If you are an overseas candidate please check here that you hold the equivalent grades before applying.
For postgraduate study in HetSys, the term “overseas” or “international” student refers to anyone who does not qualify for UK home fee status. This includes applicants from the European Union (EU), European Economic Area (EEA), and Switzerland, unless they hold settled or pre-settled status under the UK’s EU Settlement Scheme.
If you are a European applicant without UK residency or immigration status that qualifies you for home fees, you will be classified as an overseas student.