Status: PhD Student
Title of thesis: Modelling and Control of Energy Systems using Physics-based and Data-driven Approaches
Research Topic / Interests: My research focuses on the modelling, estimation, control, and optimisation of energy systems by combining physics-based models with data-driven and machine-learning approaches. My main interests include battery modelling and state estimation, system identification, physics-informed and theory-guided machine learning, data-driven control, energy forecasting, and optimisation. I am particularly interested in developing hybrid modelling frameworks that integrate physical knowledge with artificial intelligence to improve model interpretability, accuracy, and robustness for real-world energy and control applications.
ARIAC Work Package: WP3 - integration of models and AI: theory-guided/physics-guided AI, digital twins, simulated data & reinforcement learning, simulation-based inference, ML for optimisation, optimisation for ML.