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Artificial Intelligence driven multi-physics phase field fracture simulations for composites


Supervisors: Dr. Emmanouil Kakouris (Eng.), Dr. Lukasz Figiel (WMG)


Composites are widely adopted by automotive, aeronautical, and structural engineering due to their enhanced properties, yet their complex heterogeneous structure presents several challenges. Fracture is recognised as the main one, as it impacts composite safety, and when coupled with other physics, can lead to complex thermo-mechanical damage/failure scenarios. Commercially viable composite structures demand numerical methods adept at handling such complexities. This research aims to utilise the latest computational material modelling techniques to predict complex cracking patterns in composites, followed by creating an AI-driven multi-physics model for fast structural assessments. Outcomes will include enhanced understanding of damage processes, a new approach for investigating damage processes via phase-field fracture simulations, and a method to accelerate simulations using scientific machine learning.


Composites are increasingly being adopted by the automotive, aeronautical and structural engineering communities for their improved physical and mechanical properties. Such materials often possess highly heterogeneous material descriptions and tessellated/complex geometries. However, fracture poses a significant challenge for numerous composites. In practical applications, fracture is coupled with other involved physics, significantly impacting the progression of damage within composites. For instance, consider a scenario where a solid material experiences a sudden temperature change. The resulting thermal stress, caused by non-uniform thermal expansion, can lead to breakage if it exceeds the material's fracture strength. This event is commonly referred to as thermal fracture or thermal shock. Examples of such cracking include micro cracking of two-phase composite materials when a mismatch of the coefficient of thermal expansion exists between the matrix and the fibres.

Deploying commercially viable composite structures requires numerical methods that can adeptly and efficiently handle these complexities within the prescribed design iterations. Classical numerical methods, though versatile, become costly in computations when dealing with heterogeneous inclusions, large deformations and frictional contact.

Project Objectives for the PhD project:


This research aims to utilise the latest advances of computational material modelling to develop a robust thermo-mechanical damage modelling approach for predicting complex cracks in composites. Then, an AI driven multi-physics model will be created, paving the way for developing the next generation of “online”, fast and robust structural assessment tools for industrial complex composite structures under varying operating conditions.


  • Enhanced understanding of multi-physics induced phase field fracture simulations for composites, demonstrating effectiveness in solving industrial problems.
  • Developed a new multi-physics phase-field approach to investigate microscale damage processes in composites.
  • Established a method to reduce the size of coupled multi-physics models, leading to accelerated simulations for cases with unknown crack paths. This will be achieved by leveraging the latest advances in scientific machine learning.

Skills that the student will acquire:

  • Continum mechanics theory.
  • Using machine learning to accelerate the multi-physics simulations.
  • Probabilistic methods for investigating the uncertainties of the model.
  • Software development (Python, Fortran, C/C++).

To discuss this project further contact:

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At the University of Warwick, we strongly value equity, diversity and inclusion, and HetSys will provide a healthy working environment, dedicated to outstanding scientific guidance, mentorship and personal development.

HetSys is proud to be a part of the Engineering Department which holds an Athena SWAN Silver award, a national initiative to promote gender equality for all staff and students.