Edit content blocks

Dr Emmanouil Kakouris

Assistant Professor 

Emmanouil dot Kakouris at warwick dot ac dot uk

LinkedIn Google Scholar ORCID

Biography

Dr Emmanouil Kakouris is Assistant Professor in Civil Engineering in the School of Engineering at the University of Warwick.

He received his PhD from the University of Nottingham in 2019. His doctoral research project was on computational modelling of fracture in materials and structures. After his PhD, he continued in the University of Nottingham as a research associate for a collaborative research project with Schlumberger, to investigate the injection-induced vibrations due to hydraulic fracturing process.

He also holds a MEng in Civil Engineering (2012) and a MSc in Analysis and Design of Earthquake Resistant Structures (2014) from the National Technical University of Athens, Greece, where he graduated with distinction.

Following completing his PhD, he spent 4 years in the industry as a Research & Design Engineer at Roughan & O’Donovan (ROD) Consulting Engineers, Ireland, working on European and International research projects and large scale transport infrastructure commercial projects in Ireland and the UK.

He is also a peer reviewer for a number of top ranking research journals in his field such as Computer Methods in Applied Mechanics and Engineering (Elsevier), International Journal for Numerical Methods in Engineering (Wiley), Archives of Applied Mechanics (Springer) and Computational Methods in Structural Engineering (Frontiers).

He teaches modules in the subjects of civil engineering design. His research interests focus on computational science with an emphasis on computational mechanics and data science in engineering as well as the development of automated civil engineering systems.

Office

A323

Advice and feedback hours

Online via MS Teams, or by appointment.

Write to

School of Engineering, University of Warwick, Coventry, CV4 7AL

Research Interests

• Computational mechanics
• Damage modelling in materials
• Multiscale modelling (computational homogenization)
• Artificial intelligence and machine learning methods
• Intelligent engineering systems
• Asset management
• Probabilistic evaluation of systems and their uncertain behaviour
• Resilience, vulnerability and risk assessment of critical infrastructure

Teaching Interests

Teaching for full time undergraduate students and Degree Apprentices.

• ES3G8 Integrated Project (Module Leader)

Selected Publications

• Connolly L., Bernardini I., Kakouris E.G., Kelly J., (2022), "Bridge and tunnel strikes by oversized vehicles", Technical note: World Road Association (PIARC).
• Triantafyllou S.P., Kakouris E.G., (2020), "A generalised phase‐field multi‐scale finite element method for brittle fracture", International Journal for Numerical Methods in Engineering, 121, pp. 1915-1945.
• Egger A., Pillai U., Agathos K., Kakouris E.G., Chatzi E.N., Aschroft I.A., Triantafyllou S.P. (2019), "Discrete and Phase Field Methods for Linear Elastic Fracture Mechanics: A Comparative Study and State-of-the-Art Review", Applied Sciences, 9(12), 2436.
• Kakouris E.G., Triantafyllou S.P. (2019), "Phase-field material point method for dynamic brittle fracture with isotropic and anisotropic surface energy", Computer Methods in Applied Mechanics and Engineering, 357, 112503.
• Kakouris E.G., Triantafyllou S.P. (2018), "Material point method for crack propagation in anisotropic media: a phase-field approach", Achieve of Applied Mechanics, 88(1-2), pp. 287-316.
• Kakouris E.G., Triantafyllou S.P. (2017), "Phase-field material point method for brittle fracture", International Journal for Numerical Methods in Engineering, 112, pp. 1750-1776.

Research Projects

• A European Doctoral network on Advanced Meta-materials and Meta-Structures for Adaptable, Resilient and Sustainable renewable energy power plants (Met2Adapt). Funded by European Commission under Horizon Europe; 2026 - 2030; € 4,154,903,64.
• Probabilistic damage assessment of additively manufactured materials (P-DAM). Funded by Monash-Warwick Alliance; 2025.
• Artificial Intelligence driven multi-physics phase field fracture simulations for composites. Funded by EPSRC; 2024 - 2028.
• Artificial Intelligence driven multiscale material damage modelling. Funded by EPSRC; 2024 - 2028.
• Blending ultrasound data with physics-based models to predict damage in structural systems. Funded by EPSRC; 2023 - 2027.
• Infrastructure climate change risk considering interdependencies and cascading hazards (INFRALIC). Funded by Environmental Protection Agency (EPA) Ireland; 2022; €70,000.
• Bridge and tunnels strikes by oversized vehicles. Funded by World Road Association (PIARC); 2021; €40,000.
• Friction After Polishing (FAP)/ Network Safety Assessment. Funded by Transport Infrastructure Ireland (TII); 2020 - 2021; €50,000.
• Advanced options for authorities in light of Automation and Digitalisation Horizon 2040 (DIRIZON). Funded by European Conference of Directors of Roads (CEDR); 2018 - 2020; €500,000.
• Safety of Transport Infrastructure on the TEN-T Network (SAFE-10-T). Funded by European Commission under Horizon 2020; 2017 - 2020; €3,000,000.
• Predictive modelling of Injection-Induced vibrations due to Fracking (I²FRAC). Funded by Hermes Fellowship, University of Nottingham; 2018; £20,000.
• Hybrid multiscale methods for nonlinear dynamic processes. Funded by University of Nottingham; 2015 - 2018; £45,000.

Prospective Collaborators

I am always looking for high quality PhD students or collaborators to work on current or new research projects. Please directly contact if interested.

Please also check the PhD scholarships for 2025-26 entry:

1. Chancellor’s International Scholarships (CIS). Details in this link.
Deadline TBC.

2. China Scholarship Council (CSC). Details in this link.
Deadline TBC.

3. Monash Warwick Alliance Joint PhD Scholarships. Details in this link.
Deadline TBC.

4. Shanghai Jiao Tong University, SJTU- Warwick Joint PhD Scholarships. Details in this link.
Deadline TBC.

Vacancies

Looking to take the next step in your research journey? Our group is offering four exciting doctoral projects starting April 2026 or September 2026, tackling some of the biggest challenges in materials, mechanics, and infrastructure engineering.

Ph.D. position 1: Development of deep learning-based surrogate models for predicting failure in meta-material composites

Qualification:
Doctor of Philosophy in Engineering (PhD)

Start date:
Earlier possible start 5th October 2026

Funding for:
3 years

Supervisor:
Dr Emmanouil Kakouris

Application deadline:
31st July 2026. Applications are reviewed continuously, and the post may close once a suitable candidate is identified, early submission is strongly encouraged.

Why choose this PhD?
The School of Engineering at the University of Warwick invites applications for a PhD position focused on developing next-generation, machine learning, based models for predicting damage in advanced meta-material composites.

This fellowship is part of the MSCA Doctoral Network Met2Adapt "A European Doctoral network on Advanced Meta-materials and Meta-Structures for Adaptable, Resilient and Sustainable renewable energy power plants". Met2Adapt puts forward an ambitious research and training plan that will foster a new generation of researchers able to design and deliver sustainable meta-materials for vibration mitigation, self-aware meta-components and eventually carbon-efficient yet safe meta-structures for the renewable energy sector. Further details about the project can be found here.

Modern materials such as meta-material composites [1, 2] are transforming renewable energy technologies because they are both extremely strong and lightweight. However, predicting when these materials might crack or fail under changing loads remains a major challenge. In this PhD, you will have the opportunity to push scientific boundaries by developing novel computer models that accurately simulate material damage and rapidly predict future failure scenarios without costly simulations. Using the latest advances in scientific machine learning, you will contribute to the development of smarter, safer, and more sustainable engineering solutions for the next generation of renewable technologies.
This project offers the opportunity to tackle one of the most exciting challenges in modern engineering, i.e. understanding and predicting how advanced composite materials become damaged.

• Develop a high-fidelity simulation framework to model the initiation and growth of damage in complex meta-material composites under realistic loading conditions.
• Build a scientific machine learning surrogate capable of reproducing and predicting unseen damage and fracture scenarios within seconds once trained [3, 4, 5].
• Demonstrate how these intelligent models can be used for real-time structural monitoring and reliability assessment in applications such as wind turbine components.

Through this PhD, you will gain advanced expertise and transferable skills that will prepare you for a successful career in academia, research, or industry, while becoming part of the collaborative community at the forefront of computational science and engineering. You will:

• Master state-of-the-art methods in computational modelling, scientific machine learning, and data-driven computational mechanics.
• Gain hands-on experience using high-performance computing and developing scientific software to tackle real-world engineering challenges.
• Strengthen your communication, leadership, and collaboration skills through interdisciplinary training, equipping you to become a confident innovator and future leader in your field.

[1] Lincoln et al. Multifunct. Mater. 2 (2019) 043001, https://doi.org/10.1088/2399-7532/ab5242
[2] Karapiperis et al. Commun. Eng. 2 (2023) 32, https://doi.org/10.3929/ethz-b-000608722
[3] Perera et al., Comput. Methods Appl. Mech. Eng. 429 (2024) 117152, https://doi.org/10.1016/j.cma.2024.117152
[4] Feng et al., Comput. Methods Appl. Mech. Eng. 429 (2024) 117152, https://doi.org/10.1016/j.cma.2024.117152
[5] Storm et al., Comput. Methods Appl. Mech. Eng. 427 (2024) 117001, https://doi.org/10.1016/j.cma.2024.117001

What you’ll gain

• 🚀 Deep knowledge of fracture mechanics and continuum theory.
• 🤖 Hands-on experience with machine learning in engineering.
• 💻 Strong programming skills (Python, Matlab) and software development practices.
• 🌍 Internationally competitive training at one of the UK’s top engineering schools.
• 🎓 💼 A strong research profile for future academic or industry careers.

Scholarship:
The MSCA Doctoral Network fellowship is for three years and provides generous remuneration in line with the EC rules for grant holders. This fellowship is open to both Home (UK) and International candidates.

Who should apply
You have (or expect to have) a First-class or 2:1 degree in engineering, applied mathematics, physical sciences, or computational sciences. Motivation to work on computational modelling, simulation, and engineering mechanics is essential. Experience in fracture modelling, numerical methods, is a bonus - not a requirement. Curiosity and commitment matter most.

How to apply:
Submit a formal application via Warwick:
https://warwick.ac.uk/fac/sci/eng/postgraduate/applypgr/

Application form 'Course search':
Department: School of Engineering
Academic Year: 2025/26
Type of Course: Postgraduate Research
Engineering (MPhil/PhD) (P-H1Q2)
In the application form funding section, enter: Source: EK-Mechanics

Ph.D. position 2: Cracks and Code: From High-Fidelity Simulations to Fast Scientific Machine Learning Models

Qualification:
Doctor of Philosophy in Engineering (PhD)

Start date:
September 2026

Funding for:
4 years

Supervisor:
Dr Emmanouil Kakouris and Dr Peter Brommer

Application deadline:
28th January 2026. Applications are reviewed continuously, and the post may close once a suitable candidate is identified, early submission is strongly encouraged.

Why choose this PhD?
Are you excited by the idea of using advanced simulations and AI to solve real engineering problems? In this PhD, you’ll investigate how and why metals break when they are pushed to their limits, i.e. during impacts, shocks, or fast deformation. These failures are still hard to predict, and understanding them is crucial for designing safer engineering structures. You’ll combine physics-based models with modern machine-learning tools to create much faster and smarter ways of predicting when materials will fail. If you enjoy coding, problem-solving, and understanding how things break, this project offers a unique chance to make real scientific progress while collaborating with industry.

Further details can be found in https://warwick.ac.uk/fac/sci/hetsys/themes/projects2026/2026-001.

What you’ll gain

• 🔍 A solid understanding of how cracks form and grow in materials.
• 💻 Experience with advanced simulations and high-performance computing.
• 🤖 Practical machine-learning skills applied to real scientific problems.
• 🛠️ Strong coding and scientific software skills.
• 🌱 A supportive training environment at HetSys.

Training:
This PhD position also provides excellent training opportunities as part of the EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems (HetSys CDT). Further details can be found at https://warwick.ac.uk/fac/sci/hetsys/apply/training/.

Scholarship:
The award covers UK-rate tuition (£5,006/year) plus a tax-free stipend of £20,780/year for 4 years of full-time study. International candidates are welcome to apply.

Who should apply
You have (or expect to have) a First-class or 2:1 degree in engineering, applied mathematics, physical sciences, or computational sciences. Motivation to work on computational modelling, simulation, and AI/ML in engineering is essential. Experience in fracture modelling, numerical methods, is a bonus - not a requirement. Curiosity and commitment matter most.

How to apply:
Submit a formal application via Warwick:
https://warwick.ac.uk/fac/sci/hetsys/apply/howtoapply/ 

Ph.D. position 3: AI-driven decision-support for sustainable infrastructure

Qualification:
Doctor of Philosophy in Engineering (PhD)

Start date:
April 2026 or September 2026

Funding for:
4 years

Supervisor:
Dr Emmanouil Kakouris

Application deadline:
31st January 2026. Applications are reviewed continuously, and the post may close once a suitable candidate is identified, early submission is strongly encouraged.

Why choose this PhD?
How can AI help engineers make smarter, more sustainable decisions about infrastructure? In this PhD, you will develop AI-driven decision-support analytics to guide the life-cycle assessment of civil structures - from design, to operation, to end-of-life. The project addresses key global challenges: sustainability, resilience, and long-term performance of civil infrastructure. Using machine learning, probabilistic methods, and real-time data, you will create tools that help engineers, policymakers, and infrastructure managers balance structural safety, environmental impact, and cost. This is an opportunity to work at the intersection of civil engineering, artificial intelligence, and decision sciences, collaborating with experts at the University of Warwick and TU Delft (AiDAPT).

What you’ll gain

• 🧠 Expertise in decision-making theory and life-cycle assessment.
• 🔢 Skills in probabilistic modelling and managing uncertainty in engineering systems.
• 🤖 Hands-on experience applying machine learning to civil engineering problems.
• 💻 Strong programming and development skills (Python, MATLAB).
• 🌍 A unique research profile tackling global challenges in sustainability and resilience.

Scholarship:
Please note: admission does not automatically include funding. Applicants are encouraged to explore support options via the scholarships & funding.

Who should apply
This project is for you if you have (or expect to have) a First-class or 2:1 degree in engineering, applied mathematics, physical sciences, computational sciences. A strong interest in AI, machine learning, decision-making, and sustainable infrastructure is essential. Prior experience with probabilistic methods, life-cycle assessment, or structural analysis is beneficial but not required.

How to apply:
Submit a formal application via Warwick:
https://warwick.ac.uk/fac/sci/eng/postgraduate/applypgr/

Application form 'Course search':
Department: School of Engineering
Academic Year: 2025/26
Type of Course: Postgraduate Research
Engineering (MPhil/PhD) (P-H1Q2)
In the application form funding section, enter: Source: EK-Prescriptive analytics

Proposing your own project idea?

Curious about joining us or even proposing your own project idea? Explore Warwick’s postgraduate research options here:

https://warwick.ac.uk/fac/sci/eng/postgraduate/applypgr/

Questions?

Get in touch: Emmanouil.Kakouris@warwick.ac.uk

s