Rate My Professor Suresh Perinpanayagam

His research specializations encompass digital and data-centric engineering, digital twins, artificial intelligence, advanced modelling and simulation, data analytics for next-generation systems including fusion energy, electric and hydrogen aircraft, and autonomous transport vehicles. Key areas include prognostics and health management (PHM), predictive maintenance, condition monitoring, fault detection, Industry 4.0 applications for oil and gas, aircraft health monitoring systems, non-destructive testing for aircraft maintenance, additive manufacturing, physics of failure (PoF), and power electronics reliability. He received the Best Paper Award for 'Realising Duality Principle for Prognostic Models' at the 9th International Multi-Conference on Computing in the Global Information Technology (2014). Influential publications include 'The present and future of additive manufacturing in the aerospace sector: A review of important aspects' (2015, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 743 citations); 'A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery' (2016, Advances in Mechanical Engineering, 371 citations); 'Digital twin in aerospace industry: A gentle introduction' (2021, IEEE Access, 358 citations); 'A road map for reliable power electronics for more electric aircraft' (2021, Progress in Aerospace Sciences, 90 citations); and recent works such as 'Physics-augmented neural controlled differential equations for lithium-ion battery state-of-health prediction under missing cycling data' (2026, Journal of Power Sources) and 'Digital Twins in Fusion Energy Research: Current State and Future Directions' (2025, IEEE Access). With over 2,900 citations on Google Scholar, his work has substantial impact on intelligent systems, predictive analytics, and machine learning in engineering.