Sathiskumar Anusuya Ponnusami
Dr Sathiskumar Anusuya Ponnusami is a Senior Lecturer (Associate Professor) in Engineering AI and Programme Director of Materials Engineering in the School of Engineering and Materials Science at Queen Mary University of London, UK. His research lies at the intersection of computational mechanics, advanced materials and artificial intelligence, with particular interests in fracture and damage mechanics, multiscale and multiphysics modelling, composite and ceramic materials, and mechanics-guided machine learning.
He obtained his PhD from Delft University of Technology (TU Delft), the Netherlands, where his research focused on computational modelling of fracture, crack healing and lifetime prediction of self-healing ceramic systems, including thermal barrier coatings. He subsequently worked as a postdoctoral researcher in the Rolls-Royce University Technology Centre at the University of Oxford, developing experimentally informed computational models for dynamic damage and fracture in composite materials.
His current research develops mechanics-guided AI and hybrid computational frameworks for predicting failure, accelerating physics-based simulations, and enabling the design of advanced material systems. He has published over 45 peer-reviewed journal articles, and has secured research funding from EPSRC, the Royal Society, the British Council and industry. His previous recognitions include the Young Scientist Award from the International Conference on Self-healing Materials, the Dutch Aerospace Award, the Airbus-UNESCO Fly Your Ideas global innovation award, and an Alan Turing Institute UK-Italy AI research award. He is a Fellow of the Royal Aeronautical Society (FRAeS) and the UK Higher Education Academy, and serves as an Associate Editor of Mechanics of Materials and Ceramics International. Within ACerS, he co-organises symposia at ICACC and HT-CMC.
Abstract: From Fracture Mechanics to Mechanics-Guided AI: Predictive Modelling and Design of Advanced Ceramics and Composites
Predictive modelling of the mechanical behaviour and failure of advanced materials remains a challenge even in today’s world of data and compute power. This is primarily due to the inherently complex microstructures, interacting physical mechanisms and processes spanning multiple spatial and temporal scales. Over the years, our research has addressed these challenges by combining computational mechanics, multiscale and multiphysics modelling and, more recently, artificial intelligence.
This lecture will present this progression from physics-based models to the increasing need for data-driven approaches through selected examples. It will first present computational damage and crack-healing models developed to analyse and design self-healing ceramic systems, followed by lifetime prediction of self-healing thermal barrier coatings under uncertainty. It will then highlight multiscale computational approaches for understanding damage and fracture in ceramics and composite materials. Finally, it will cover our recent efforts to integrate machine learning with mechanics-based models, including data-driven prediction of material behaviour and hybrid deep-learning–finite-element frameworks for accelerating fracture simulations in advanced material systems.
Across these developments, a recurring theme is that data-driven models should complement rather than replace physical modelling. The lecture will conclude with opportunities for developing interpretable, generalisable and physics-constrained AI tools that retain the predictive capabilities of computational mechanics while enabling efficient exploration and design of next-generation ceramics, composites and other complex material systems.