About Us 

Our open-source simulation software tool, PhAST (Git link here) for fracture modelling is released. It is a built in PyTorch. It can run on GPU, CPU and. Built on PyTorch, PhAST is a matrix-free, differentiable solver for simulating  phase-field fracture problems without assembling large global matrices. It is designed to be differentiable, making it easier to combine with machine learning techniques. Congratulations to Allamaprabhu Ani, a PhD student in our group on this amazing development. Preprint of the manuscript is availble here in arXiv.

Data science is best learnt when the data matters to you. This is the philosophy behind the second year core engineering module on Machine Learning designed and taught by Dr Sathiskumar Ponnusami. The goal is simple: every student finishes having trained machine learning models on data they could genuinely encounter in their first engineering role. That connection to real engineering practice is the whole point. Dr Ponnusami designed interactive, animation-rich Python notebooks that let students explore and experiment at their own pace.

Our group was represented at AIAA SciTech with three presentations ranging from nonlinear buckling of sandwich structures to lattice structures and AI-driven structural health monitoring. Dr Villorthan Sunthareswaran (PhD student from our group) successfully delivered the presentations.

We hosted Dr Mrityunjay Singh, Chief Scientist and the past President of the American Ceramic Society at our Group. He delivered a lecture on sustainable manufacturing of advanced materials and structures to an audience of PhD students, post-docs and academics from engineering.

Our Group successfully organised the second Mechanics and Machine Learning Workshop (M2L) in July 2025. Invited talks were delivered by Prof. JF Molinari (EPFL), Prof. Zahra Sharif Khodaei (Imperial College), Prof. Ghatu Subhash (Uni. of Florida), Dr Souvik Chakraborty (IIT Delhi), Dr Sathis Ponnusami (City) and Dr Sid Kumar (TU Delft).  Supported by UKRI-EPSRC and the British Council, the interactive hybrid event was well-attended by over 70 researchers across the UK, Europe, USA and India.

Dr Sathiskumar Ponnusami delivered an invited talk at the Machine Learning for Mechanics workshop at Queen Mary, University of London, sponsored by the Heilbronn Institute for Mathematical Research. His talk focussed on how physical simulations and machine learning models should go hand-in-hand to realise reliable and faster simulations in solid mechanics and materials engineering problems. The workshop involved talks by academics from other universities such as Imperial College, Queen Mary, and Brunel University. 

Dr Sathiskumar Ponnusami delivered an invited seminar (Solid Mechanics and Materials Seminar) at the University of Oxford. He spoke about his research on multifunctionalities in aerospace composites, while emphasising the effectiveness of modelling and simulation tools for the design of such multifunctional material/structural systems. We had great discussions with colleagues around potential collaborations on the mechanics of high-temperature materials and composites.

Dr Sathiskumar Ponnusami was invited by the UK Foreign Office to the British Ambassador’s Residence (Villa Wolkonsky) in Rome to participate in a panel discussion and present at a UK-Italy AI event. The purpose is to foster collaboration between the UK and Italy in AI with an increasing emphasis on the recent UK-Italy joint declaration and MoU on Bilateral Cooperation.  The event was organised by the British Embassy in Rome and the UK Science and Innovation Network, in collaboration with the Alan Turing Institute, Italy’s Future AI research Institute (FAIR), and the Italian Ministry of Research and Universities. 

Sathiskumar was an awardee of the UK-Italy Trustworthy AI Exchange Researcher programme 2023/2024, coordinated by The Alan Turing Institute and the UK Foreign Office, to develop such collaborations with Italy. He presented his research on AI applications for the design of aerospace materials, structures and systems. In the invited panel discussion,  he highlighted the routes to establishing collaborations between the UK and Italy in Applied AI through Horizon Europe. 

Our Group organised the first workshop on 'Mechanics and Machine Learning'. The workshop was attended by more than 50 researchers with invited talks delivered by academics from City (Dr Ponnusami and Dr Yan), EPFL (Prof. Molinari and Dr Garcia Suarez), and the University of Florida (Prof. Subhash). Academics from EPFL and Florida spend a month at our Group exchanging research ideas and having active discussions.

Dr Sathiskumar Ponnusami is appointed as an Associate Editor for Mechanics of Materials Journal (MECMAT) effective from January 2024.  MECMAT (2025 IF 4.1), established in 1982, is one of the classical decades-old mechanics journals publishing articles on flow, fracture, and constitutive behaviour of materials.